Publisher：Empirical Software Engineering  

Recent studies have demonstrated remarkable advancements in source code learning, which applies deep neural networks (DNNs) to tackle various software engineering tasks. Similar to other DNN-based domains, source code learning also requires massive high-quality training data to achieve the success of these applications. Data augmentation, a technique used to produce additional training data, is widely adopted in other domains (e.g. computer vision). However, the existing practice of data augmentation in source code learning is limited to simple syntax-preserved methods, such as code refactoring. In this paper, considering that source code can also be represented as text data, we take an early step to investigate the effectiveness of data augmentation methods originally designed for natural language texts in the context of source code learning. To this end, we focus on code classification tasks and conduct a comprehensive empirical study across four critical code problems and four DNN architectures to assess the effectiveness of 25 data augmentation methods. Our results reveal specific data augmentation methods that yield more accurate and robust models for source code learning. Additionally, we discover that the data augmentation methods remain beneficial even when they slightly break source code syntax.

DOI： 10.1007/s10664-025-10624-2

Web of Science

Scopus

Publisher：ACM Transactions on Software Engineering and Methodology  

Cyber-physical systems (CPSs) are increasingly adopting deep neural networks (DNNs) as controllers, giving birth to AI-enabled CPSs. Despite their advantages, many concerns arise about the safety of DNN controllers. Numerous efforts have been made to detect system executions that violate safety specifications; however, once a violation is detected, to fix the issue, it is necessary to localise the parameters of the DNN controller responsible for the wrong decisions leading to the violation. This is particularly challenging, as it requires to consider a sequence of control decisions, rather than a single one, preceding the violation. To tackle this problem, we propose SpectAcle, that can localise the faulty parameters in DNN controllers. SpectAcle considers the DNN inferences preceding the specification violation and uses forward impact to determine the DNN parameters that are more relevant to the DNN outputs. Then, it identifies which of these parameters are responsible for the specification violation, by adapting classic suspiciousness metrics. Moreover, we propose two versions of SpectAcle, that consider differently the timestamps that precede the specification violation. We experimentally evaluate the effectiveness of SpectAcle on 6,067 faulty benchmarks, spanning over different application domains. The results show that SpectAcle can detect most of the faults.

DOI： 10.1145/3705307

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Scopus

Publisher：Proceedings of the ACM on Programming Languages  

Incremental verification is an emerging neural network verification approach that aims to accelerate the verification of a neural network N^∗ by reusing the existing verification result (called a template) of a similar neural network N. To date, the state-of-the-art incremental verification approach leverages the problem splitting history produced by branch and bound (BaB) in verification of N, to select only a part of the sub-problems for verification of N^∗, thus more efficient than verifying N^∗ from scratch. While this approach identifies whether each sub-problem should be re-assessed, it neglects the information of how necessary each sub-problem should be re-assessed, in the sense that the sub-problems that are more likely to contain counterexamples should be prioritized, in order to terminate the verification process as soon as a counterexample is detected. To bridge this gap, we first define a counterexample potentiality order over different sub-problems based on the template, and then we propose Olive, an incremental verification approach that explores the sub-problems of verifying N^∗ orderly guided by counterexample potentiality. Specifically, Olive has two variants, including Olive^g, a greedy strategy that always prefers to exploit the sub-problems that are more likely to contain counterexamples, and Olive^b, a balanced strategy that also explores the sub-problems that are less likely, in case the template is not sufficiently precise. We experimentally evaluate the efficiency of Olive on 1445 verification problem instances derived from 15 neural networks spanning over two datasets MNIST and CIFAR-10. Our evaluation demonstrates significant performance advantages of Olive over state-of-the-art classic verification and incremental approaches. In particular, Olive shows evident superiority on the problem instances that contain counterexamples, and performs as well as Ivan on the certified problem instances.

DOI： 10.1145/3720417

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Publisher：Journal of Systems and Software  

Quantum Intermediate Representation (QIR) is an LLVM-based intermediary representation developed by Microsoft for quantum program compilers. QIR's objective is to serve as a versatile solution for quantum program compilers, irrespective of the programming languages used at the front end and the hardware utilized at the back end. This approach minimizes redundant development efforts involving intermediary representations and compilers. Currently, QIR remains in the development phase and is described informally in natural language, lacking a formal definition. This informal description leads to interpretational ambiguity and a shortage of precision when implementing quantum functions. Our work aims to address this gap by providing formal definitions for QIR's data types and instruction sets. We strive to establish correctness and safety assurances for operations and intermediate code conversions within the QIR framework. To substantiate our design, we present potentially unsafe QIR code instances that our formal approach can detect and rectify. This contribution enhances the reliability and robustness of quantum program development within the QIR context.

DOI： 10.1016/j.jss.2024.112236

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Scopus

Publisher：IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems  

Falsification, whose aim is to detect unsafe behaviors of cyber-physical systems (CPS) that violate signal temporal logic (STL) specifications, has been actively investigated in the past decade. Although numerous falsification approaches have been proposed, the falsification community suffers from a shortage of benchmarks that hinders a thorough assessment of those falsification approaches. In this paper, we bridge this gap by proposing an automated approach for generating falsification benchmarks. Our approach is data-driven: firstly, we generate different time-variant traces (acting as system output traces) that satisfy a given STL specification, and we associate these with corresponding system input traces; then, we use these input and output traces to train an LSTM model that generalizes them. These models can serve as benchmarks for assessing falsification approaches against the given specification. In the experimental evaluation, we validate the generated models by measuring their ability to differentiate the performance of different falsification approaches. Our generated models expose strengths and weaknesses of all the considered falsification approaches, which was not achieved by benchmarks currently used in the falsification community. These results demonstrate the usefulness of our approach and can potentially push forward subsequent research in falsification.

DOI： 10.1109/TCAD.2025.3550410

Scopus

Publisher：IEEE Access  

Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. For example, adversarial examples can fool deep learning systems easily by perturbing inputs with small, imperceptible noises. There has been extensive research regarding the generation of and defense against adversarial examples in computer vision tasks, and existing attacking methods based on optimization fall into two categories: maximizing the loss and minimizing the perturbation size. To solve the optimization problem for generating adversarial examples, the latter approach incorporates a misclassifying constraint into the objective using a Lagrangian multiplier or penalty parameter, usually determined by binary search. However, this is relatively inefficient because the parameter varies for each input. To address this inefficiency, based on the penalty method, also called the sequential unconstrained minimization technique, we propose PenaltyAttack. Unlike traditional methods, it generates white-box ℓ<inf>2</inf> and ℓ<inf>1</inf> adversarial examples by progressively increasing the penalty parameter instead of employing binary search. Extensive experiments on three test benches (MNIST, CIFAR10, and ImageNet) demonstrate that compared with existing methods, our attack can generate adversarial examples with minor perturbations at a higher success rate. The implementation and experimental code are publicly available at https://github.com/sjysjy1/PenaltyMethodAttack

DOI： 10.1109/ACCESS.2025.3529217

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Scopus

Publisher：Journal of Systems and Software  

Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling layers that use graph pooling operators (e.g., Max-pooling) to effectively reduce the number of nodes while preserving the semantic information of the graph. Recently, to enhance GNNs in graph learning tasks, Manifold-Mixup, a data augmentation technique that produces synthetic graph data by linearly mixing a pair of graph data and their labels, has been widely adopted. However, the performance of Manifold-Mixup can be highly affected by graph pooling operators, and there have not been many studies that are dedicated to uncovering such affection. To bridge this gap, we take an early step to explore how graph pooling operators affect the performance of Mixup-based graph learning. To that end, we conduct a comprehensive empirical study by applying Manifold-Mixup to a formal characterization of graph pooling based on 11 graph pooling operations (9 hybrid pooling operators, 2 non-hybrid pooling operators). The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (JAVA250, Python800) demonstrate that hybrid pooling operators are more effective for Manifold-Mixup than the standard Max-pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of producing more accurate and robust GNN models. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

DOI： 10.1016/j.jss.2024.112139

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Scopus

Publisher：ACM Transactions on Software Engineering and Methodology  

Quantum computing has emerged as a promising field with the potential to revolutionize various domains by harnessing the principles of quantum mechanics. As quantum hardware and algorithms continue to advance, developing high-quality quantum software has become crucial. However, testing quantum programs poses unique challenges due to the distinctive characteristics of quantum systems and the complexity of multi-subroutine programs. This article addresses the specific testing requirements of multi-subroutine quantum programs. We begin by investigating critical properties by surveying existing quantum libraries and providing insights into the challenges of testing these programs. Building upon this understanding, we focus on testing criteria and techniques based on the whole testing process perspective, spanning from unit testing to integration testing. We delve into various aspects, including IO analysis, quantum relation checking, structural testing, behavior testing, integration of subroutine pairs, and test case generation. We also introduce novel testing principles and criteria to guide the testing process. We conduct comprehensive testing on typical quantum subroutines, including diverse mutants and randomized inputs, to evaluate our proposed approach. The analysis of failures provides valuable insights into the effectiveness of our testing methodology. Additionally, we present case studies on representative multi-subroutine quantum programs, demonstrating the practical application and effectiveness of our proposed testing principles and criteria.

DOI： 10.1145/3656339

Web of Science

Scopus

Publisher：ACM Transactions on Software Engineering and Methodology  

Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this article studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover's algorithm). We first introduce our oracle implementation in Grover's algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic (), yielding better results than the upper bound (O(N³/log N)) of existing classical algorithms. Finally, we conducted a classical simulation of Grover's search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.

DOI： 10.1145/3644389

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Scopus

Publisher：Journal of Systems and Software  

Quantum programs exhibit inherent non-deterministic behavior, which poses more significant challenges for error discovery compared to classical programs. While several testing methods have been proposed for quantum programs, they often overlook fundamental questions in black-box testing. In this paper, we bridge this gap by presenting three novel algorithms specifically designed to address the challenges of equivalence, identity, and unitarity checking in black-box testing of quantum programs. We also explore optimization techniques for these algorithms, including specialized versions for equivalence and unitarity checking, and provide valuable insights into parameter selection to maximize performance and effectiveness. To evaluate the effectiveness of our proposed methods, we conducted comprehensive experimental evaluations, which demonstrate that our methods can rigorously perform equivalence, identity, and unitarity checking, offering robust support for black-box testing of quantum programs.

DOI： 10.1016/j.jss.2024.112000

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Scopus

Publisher：IEEE Transactions on Dependable and Secure Computing  

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz, a machine learning guided smart contract fuzzing framework. The machine learning models are trained with novel features (e.g., word vectors and instructions) and are used to filter likely benign program paths. Comparing with existing static tools, machine learning model is proven to be more robust, avoiding directly adopting manually-defined rules in specific tools. We compare xFuzz with three state-of-the-art tools on 7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities, of which 15 vulnerabilities are exposed for the first time. Furthermore, our approach is shown to be efficient in detecting non-cross-contract vulnerabilities as well - using less than 20% time as that of other fuzzing tools, xFuzz detects twice as many vulnerabilities.

DOI： 10.1109/TDSC.2022.3182373

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Scopus

Publisher：Knowledge Based Systems  

The methodology that employs deep learning to handle software engineering tasks, such as bug detection, is commonly referred to as source code learning. Given the inherent graph nature of source code, graph learning, bolstered by graph neural networks (GNNs), has seen an increasing adoption in the domain of source code learning. Similar to other contexts within deep learning, source code learning also relies on extensive high-quality training data, and the scarcity of such data has become a primary impediment that leads to performance bottlenecks. In practice, data augmentation is often used as a countermeasure to mitigate this issue, by synthesizing additional training data based on existing ones. However, most existing practice of data augmentation in source code learning is limited to simple program transformation methods, such as code refactoring, thus not sufficiently effective. In this work, in light of the graph nature of source code, we propose to apply the data augmentation methods used for graph-structured data in graph learning to the tasks of source code learning, and we conduct a comprehensive empirical study to evaluate whether such new data augmentation approaches bring better effectiveness, in terms of producing more accurate and robust models. Specifically, we evaluate four critical software engineering tasks and seven neural network architectures to assess the effectiveness of five data augmentation methods. Experimental results identify that, compared to the data augmentation-free training approach, the Manifold-Mixup method can significantly improve both the accuracy and robustness of the trained models of source code learning, for up to 1.60% and 4.09%, respectively.

DOI： 10.1016/j.knosys.2023.111328

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Scopus

Publisher：ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings  

Contrastive cross-modality pretraining has recently exhibited impressive success in diverse fields, whereas there is limited research on their merits in speech emotion recognition (SER). In this paper, we propose GEmo-CLAP, a kind of gender-attribute-enhanced contrastive language-audio pretraining (CLAP) method for SER. Specifically, we first construct an effective emotion CLAP (Emo-CLAP) for SER, using pre-trained text and audio encoders. Second, given the significance of gender information in SER, two novel multi-task learning based GEmo-CLAP (ML-GEmo-CLAP) and soft label based GEmo-CLAP (SL-GEmo-CLAP) models are further proposed to incorporate gender information of speech signals, forming more reasonable objectives. Experiments on IEMOCAP indicate that our proposed two GEmo-CLAPs consistently outperform Emo-CLAP with different pre-trained models. Remarkably, the proposed WavLM-based SL-GEmo-CLAP obtains the best WAR of 83.16%, which performs better than state-of-the-art SER methods.

DOI： 10.1109/ICASSP48485.2024.10448394

Web of Science

Scopus

Publisher：Proceedings 2024 IEEE International Conference on Quantum Software Qsw 2024  

The rapid development of quantum computing technology has opened new possibilities for solving complex problems beyond the capabilities of classical computing paradigms. However, combining quantum computing with classical systems to create Hybrid Quantum-Classical (HQC) software systems presents unique challenges that require novel approaches in software architecture. This paper proposes a foundation for developing Architecture Description Languages (ADLs) specifically for HQC systems. We aim to provide a robust framework for coherently describing, integrating, and analyzing quantum and classical computing elements. The paper discusses the design principles, methodology, and structural components of the architecture description languages and outlines future research directions.

DOI： 10.1109/QSW62656.2024.00016

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Scopus

Publisher：Gecco 2024 Proceedings of the 2024 Genetic and Evolutionary Computation Conference  

In AI-enabled CPSs, DNNs are used as controllers for the physical system. Despite their advantages, DNN controllers can produce wrong control decisions, which can lead to safety risks for the system. Once wrong behaviors are detected, the DNN controller should be fixed. DNN repair is a technique that allows to perform this fine-grained improvement. However, state-of-the-art DNN repair techniques require ground-truth labels to guide the repair. For AI-enabled CPSs, these are not available, as it is not possible to assess whether a specific control decision is correct. Nevertheless, it is possible to assess whether the DNN controller leads to wrong behaviors of the controlled system by considering system-level requirements. In this paper, following this observation, we propose a novel DNN repair approach that is guided by system-level specifications. The approach takes in input a system-level specification, some tests violating the specification, and some faulty DNN weights. The approach searches for alternative weight values with the goal of fixing the behavior on the failing tests without breaking the passing tests. We also propose a heuristic that allows us to accelerate the search by avoiding the execution of some tests. Experiments on real-world AI-enabled CPSs show that the approach effectively repairs their controllers.

DOI： 10.1145/3638529.3654078

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Scopus

Publisher：Proceedings 2024 IEEE International Conference on Quantum Software Qsw 2024  

Quantum computing holds the potential to revolutionize various fields by efficiently tackling complex problems. At its core are quantum circuits, sequences of quantum gates manipulating quantum states. The selection of the right quantum circuit ansatz, which defines initial circuit structures and serves as the basis for optimization techniques, is crucial in quantum algorithm design. This paper presents a categorized catalog of quantum circuit ansatzes aimed at supporting quantum algorithm design and implementation. Each ansatz is described with details such as intent, motivation, applicability, circuit diagram, implementation, example, and see also. Practical examples are provided to illustrate their application in quantum algorithm design. The catalog aims to assist quantum algorithm designers by offering insights into the strengths and limitations of different ansatzes, thereby facilitating decision-making for specific tasks.

DOI： 10.1109/QSW62656.2024.00021

Web of Science

Scopus

Publisher：Proceedings 2024 IEEE International Conference on Software Maintenance and Evolution Icsme 2024  

Deep learning (DL) compilers, such as TVM and TensorFlow, encompass a variety of passes for optimizing computation graphs (i.e., DL models). Despite the efforts on developing optimization passes, it remains a challenge in arranging these passes - most compilers employ fixed pass sequences that do not fit with computation graphs of diverse structures; on the other hand, optimization passes have cascade effects, making the structures of graphs under compilation volatile and as well making it difficult to generate optimal sequences for graphs. Inspired by recent progresses on static computing memory footprints (i.e., memory usages) of computation graphs, we introduce in this paper OPASS, a novel approach to orchestrating TVM's optimization passes for lowering memory footprints of computation graphs, and finally allowing the graphs to run on memory-constrained devices. The key idea is, given a computation graph G, to optimize the graph heuristically and iteratively: OPASS learns the effects of passes on the graph; it then optimizes G iteratively - each iteration picks up a pass by the reduction of the memory footprint of G and as well the implicit effects of the pass for further optimizations, letting the pass be applied. We evaluate OPASS on Rebench (a suite of computation graphs) and two real-world models (Transformer and ResNet). The results clearly show the strength of OPASS: it outperforms TVM's default sequence by 1.77× in reducing graphs' memory footprints, with affordable costs; it also offers extra memory reductions of 5∼ 12% by catching the implicit effects of passes. Furthermore, OPASS helps analyze positive/negative effects of passes to graphs' memory footprints, providing TVM developers with best practices for designing optimization pass sequences.

DOI： 10.1109/ICSME58944.2024.00026

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Scopus

Publisher：Proceedings 2024 IEEE ACM 5th International Workshop on Quantum Software Engineering Q Se 2024  

Automated Program Repair (APR) is a vital area in software engineering that generates automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.

DOI： 10.1145/3643667.3648223

Web of Science

Scopus

Publisher：Proceedings of 2024 IEEE Spoken Language Technology Workshop Slt 2024  

The continuous evolution of pre-trained speech models has greatly advanced Speech Emotion Recognition (SER). However, current research typically relies on utterance-level emotion labels, inadequately capturing the complexity of emotions within a single utterance. In this paper, we introduce GMP-TL, a novel SER framework that employs gender-augmented multi-scale pseudo-label (GMP) based transfer learning to mitigate this gap. Specifically, GMPTL initially uses the pre-trained HuBERT, implementing multi-task learning and multi-scale k -means clustering to acquire frame-level GMPs. Subsequently, to fully leverage frame-level GMPs and utterance-level emotion labels, a two-stage model fine-tuning approach is presented to further optimize GMP-TL. Experiments on IEMOCAP show that our GMP-TL attains a WAR of 80.0% and an UAR of 82.0%, achieving superior performance compared to state-of-the-art unimodal SER methods while also yielding comparable results to multimodal SER approaches.

DOI： 10.1109/SLT61566.2024.10832339

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Scopus

Publisher：Proceedings 6th IEEE International Conference on Artificial Intelligence Testing Aitest 2024  

It has been shown that multimodal text-vision large language models (LLMs) are vulnerable to visual adversarial examples. An adversarial example is a delicately perturbed input intended to cause deep neural networks to behave incorrectly. Currently, adversarial attacks can be categorized into two types based on the knowledge required to perform an attack: black box and white box. Most existing black-box attacks only try to generate adversarial examples within a given ℓ2% or ℓ∞% pertur-bation budget. We propose a novel black-box attack to address this research gap to generate ℓ1%, ℓ2% adversarial examples with minimal perturbation. To this end, our attack solves a constrained optimization problem by converting it to an unconstrained one and then finds its solution using the genetic algorithm. Extensive experiments on three test benches have been conducted to evaluate its performance. To facilitate reproducing the results presented in this work, we make the code for the experiments publicly available at https://github.com/sjysjy1/MPBA.

DOI： 10.1109/AITest62860.2024.00021

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Scopus

Publisher：Journal of Systems and Software  

Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. However, there is no suitable bug benchmark suite that can systematically evaluate the debugging and testing methods of quantum programs until now. This paper proposes Bugs4Q, a benchmark of forty-two real, manually validated Qiskit bugs from three popular platforms (GitHub, StackOverflow, and Stack Exchange) in programming, supplemented with test cases to reproduce buggy behaviors. Bugs4Q also provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding source code and unit tests, facilitating the reproducible empirical studies and comparisons of Qiskit program debugging and testing tools. Bugs4Q is publicly available at https://github.com/Z-928/Bugs4Q-Framework. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

DOI： 10.1016/j.jss.2023.111805

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Scopus

Publisher：Pattern Recognition  

Visual object tracking (VOT) is a fundamental computer vision task that aims to track a target in a sequence of video frames. It has been broadly adopted in safety- and security-critical applications, such as self-driving systems and traffic control systems. However, the VOT models (i.e., the trackers) that rely on third-party training resources face a severe threat of backdoor attacks, which refer to the type of the attacks that poison a portion of training data and mislead the tracker to track a wrong target. A surge of research interest has arisen in backdoor attacks in the domain of image classification, as a measure to expose the potential security risks of the classifiers and inspire new defense techniques. Despite the prosperity of the research in backdoor attacks in image classification, there still lacks investigation in backdoor attacks against VOT, due to their unique challenges: first, the architecture of a VOT model is much more complicated than that of an image classifier; second, VOT targets a sequence of video frames rather than individual images. To bridge the gap, we propose a novel and effective targeted backdoor attack approach TAT specifically against VOT tasks. In particular, TAT includes a basic version TAT-BA that can achieve effective and stealthy backdoor attacks against VOT trackers, and an advanced version TAT-DA that can evade two representative defense techniques. Our large-scale experimental evaluation demonstrates the effectiveness and the stealthiness of TAT. Moreover, we also demonstrate the performances of TAT-BA under real-world settings and the abilities of TAT-DA to counter defense techniques. The code will be available at https://github.com/MisakaZipi/TAT.

DOI： 10.1016/j.patcog.2023.109629

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Scopus

Publisher：ACM Transactions on Software Engineering and Methodology  

Over the past few years, deep neural networks (DNNs) have achieved tremendous success and have been continuously applied in many application domains. However, during the practical deployment in industrial tasks, DNNs are found to be erroneous-prone due to various reasons such as overfitting and lacking of robustness to real-world corruptions during practical usage. To address these challenges, many recent attempts have been made to repair DNNs for version updates under practical operational contexts by updating weights (i.e., network parameters) through retraining, fine-tuning, or direct weight fixing at a neural level. Nevertheless, existing solutions often neglect the effects of neural network architecture and weight relationships across neurons and layers. In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i.e., block level).We first perform empirical studies to investigate the limitation of whole network-level and layer-level repairing, which motivates us to explore a novel repairing direction for DNN repair at the block level. To this end, we need to further consider techniques to address two key technical challenges, i.e., block localization, where we should localize the targeted block that we need to fix; and how to perform joint architecture and weight repairing. Specifically, we first propose adversarial-aware spectrum analysis for vulnerable block localization that considers the neurons' status and weights' gradients in blocks during the forward and backward processes, which enables more accurate candidate block localization for repairing even under a few examples. Then, we further propose the architecture-oriented search-based repairing that relaxes the targeted block to a continuous repairing search space at higher deep feature levels. By jointly optimizing the architecture and weights in that space, we can identify a much better block architecture. We implement our proposed repairing techniques as a tool, named ArchRepair, and conduct extensive experiments to validate the proposed method. The results show that our method can not only repair but also enhance accuracy and robustness, outperforming the state-of-the-art DNN repair techniques.

DOI： 10.1145/3585005

Scopus

Publisher：IEEE Transactions on Software Engineering  

Modern Cyber-Physical Systems (CPSs) that need to perform complex control tasks (e.g., autonomous driving) are increasingly using AI-enabled controllers, mainly based on deep neural networks (DNNs). The quality assurance of such types of systems is of vital importance. However, their verification can be extremely challenging, due to their complexity and uninterpretable decision logic. Falsification is an established approach for CPS quality assurance, which, instead of attempting to prove the system correctness, aims at finding a time-variant input signal violating a formal specification describing the desired behavior; it often employs a search-based testing approach that tries to minimize the robustness of the specification, given by its quantitative semantics. However, guidance provided by robustness is mostly black-box and only related to the system output, but does not allow to understand whether the temporal internal behavior determined by multiple consecutive executions of the neural network controller has been explored sufficiently. To bridge this gap, in this paper, we make an early attempt at exploring the temporal behavior determined by the repeated executions of the neural network controllers in hybrid control systems and first propose eight time-aware coverage criteria specifically designed for neural network controllers in the context of CPS, which consider different features by design: the simple temporal activation of a neuron, the continuous activation of a neuron for a given duration, and the differential neuron activation behavior over time. Second, we introduce a falsification framework, named FalsifAI FalsifAI, that exploits the coverage information for better falsification guidance. Namely, inputs of the controller that increase the coverage (so improving the exploration of the DNN behaviors), are prioritized in the exploitation phase of robustness minimization. Our large-scale evaluation over a total of 3 typical CPS tasks, 6 system specifications, 18 DNN models and more than 12,000 experiment runs, demonstrates 1) the advantage of our proposed technique in outperforming two state-of-the-art falsification approaches, and 2) the usefulness of our proposed time-aware coverage criteria for effective falsification guidance.

DOI： 10.1109/TSE.2022.3194640

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.patcog.2023.109629

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Proceedings 2023 IEEE International Conference on Software Analysis Evolution and Reengineering Saner 2023  

Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MixCode that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MixCode on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRep1, and Refactory), and seven model architectures (including two pretrained models CodeBERT and GraphCodeBERT). Experimental results demonstrate that MixCode outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness.

DOI： 10.1109/SANER56733.2023.00043

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Scopus

Publisher：Proceedings International Conference on Software Engineering  

The fast advances in mobile hardware and widespread smartphone usage have fueled the growth of global mobile gaming in the past decade. As a result, the need for quality assurance of mobile gaming has become increasingly pressing. While general-purpose testing methods have been developed for mobile applications, they become struggling when being applied to mobile games due to the unique characteristics of mobile games, such as dynamic loading and stunning visual effects. There comes a growing industrial demand for automated testing techniques with high compatibility (compatible with various resolutions, and platforms) and non-intrusive characteristics (without packaging external modules into the source code, e.g., POCO). To fulfill these demands, in this paper, we introduce our experience in adopting the widget detection-based testing technique WDTEST, for mobile games at NetEase Games. To this end, we have constructed by far the largest graphical user interface (GUI) dataset for mobile games and conducted comprehensive evaluations on the performance of state-of-the-art widget detection techniques in the context of mobile gaming.We leverage widget detection techniques to develop WDTEST, which performs automated testing using only screenshots as input. Our evaluation shows that WDTEST outperforms the widely used tool Monkey in achieving three times more coverage of unique UI in gaming scenarios. Our further experiments demonstrate that WDTEST can be applied to general mobile applications without additional fine-tuning. Furthermore, we conducted a thorough survey at NetEase Games to gain a comprehensive understanding of widget detection-based testing techniques and identify challenges in industrial mobile game testing. The results show that testers are overall satisfied with the compatibility testing aspect of widget detection-based testing, but not much with functionality testing. This survey also highlights several unique characteristics of mobile games, providing valuable insights for future research directions.

DOI： 10.1109/ICSE-SEIP58684.2023.00021

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Scopus

Publisher：Proceedings 2023 IEEE ACM 4th International Workshop on Quantum Software Engineering Q Se 2023  

Quantum entanglement plays a crucial role in quantum computing. Entangling information has important implications for understanding the behavior of quantum programs and avoiding entanglement-induced errors. Entanglement analysis is a static code analysis technique that determines which qubit may entangle with another qubit and establishes an entanglement graph to represent the whole picture of interactions between entangled qubits. This paper presents the first static entanglement analysis method for quantum programs developed in the practical quantum programming language Q#. Our method first constructs an interprocedural control flow graph (ICFG) for a Q# program and then calculates the entanglement information not only within each module but also between modules of the program. The analysis results can help improve the reliability and security of quantum programs.

DOI： 10.1109/Q-SE59154.2023.00013

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Scopus

DOI： 10.1145/3594671.3594686

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Publisher：Proceedings 2023 IEEE International Conference on Quantum Software Qsw 2023  

Ensuring the correctness of quantum programs is crucial for quantum software quality assurance. Although various effective verification methods exist for classical programs, they cannot be applied to quantum programs due to the fundamental differences in their execution logic, such as quantum superposition and entanglement. This calls for new methods to verify the correctness of quantum programs. In this paper, we present a behavioral interface specification language (BISL) called ScaffML for the quantum programming language Scaffold. ScaffML allows the specification of pre- and post-conditions for Scaffold modules and enables the mixing of assertions with Scaffold code, thereby facilitating debugging and verification of quantum programs. This paper discusses the goals and overall approach of ScaffML and describes the basic features of the language through examples. ScaffML provides an easy-to-use specification language for quantum programmers, supporting static analysis, run-time checking, and formal verification of Scaffold programs. Finally, we present several instances to illustrate the workflow and functionalities of ScaffML.

DOI： 10.1109/QSW59989.2023.00024

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Scopus

Publisher：Proceedings 2023 38th IEEE ACM International Conference on Automated Software Engineering Ase 2023  

The recent fast development of quantum computers breaks several computation limitations that are difficult for conventional computers. Up to the present, although many approaches and tools have been proposed to test quantum programs, the fundamental features of quantum programs, i.e., magnitude, phase, and entanglement, have been largely overlooked, leading to limited fault detection capability and reduced testing effectiveness. To address this problem, we propose an automated testing framework named QURATEST, equipped with three test case generators (including two newly proposed techniques, UCNOT and IQFT in this paper, as well as one based on Random techniques) to test quantum programs. Overall, the proposed generators enable the generation of diverse test inputs by considering the quantum features of quantum programs. In the experiments, we perform an in-depth evaluation of QURATEST from three aspects: generated test case diversity, output coverage of the program under test, and fault detection capability. The results demonstrate the potential of our newly proposed techniques in that IQFT can generate the most diverse test cases regarding magnitude, phase, and entanglement, with 66% cell coverage. Comparatively, the Random approach only has 10% cell coverage. Regarding the evaluations of the output coverage, IQFT can achieve the highest output coverage in 70.2% (33 out of 47) of all quantum programs. In terms of fault detection, UCNOT outperforms the other two techniques. Specifically, the test cases generated by UCNOT have the best mutation score in 88.4% (23 out of 26) quantum programs.

DOI： 10.1109/ASE56229.2023.00196

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Publisher：Proceedings 2023 IEEE ACM 4th International Workshop on Quantum Software Engineering Q Se 2023  

Static analysis is the process of analyzing software code without executing the software. It can help find bugs and potential problems in software that may only appear at runtime. Although many static analysis tools have been developed for classical software, due to the nature of quantum programs, these existing tools are unsuitable for analyzing quantum programs. This paper presents QChecker, a static analysis tool that supports finding bugs in quantum programs in Qiskit. QChecker consists of two main modules: A module for extracting program information based on abstract syntax tree (AST), and a module for detecting bugs based on patterns. We evaluate the performance of QChecker using the Bugs4Q benchmark. The evaluation results show that QChecker can effectively detect various bugs in quantum programs.

DOI： 10.1109/Q-SE59154.2023.00014

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Publisher：11th International Conference on Learning Representations Iclr 2023  

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leverage highly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latent information from the historical behaviors (e.g., state transitions, topological similarities, etc.) and lack scalability during DRL training.This work introduces Neural Episodic Control with State Abstraction (NECSA), a simple but effective state abstraction-based episodic control containing a more comprehensive episodic memory, a novel state evaluation, and a multi-step state analysis.We evaluate our approach to the MuJoCo and Atari tasks in OpenAI gym domains.The experimental results indicate that NECSA achieves higher sample efficiency than the state-of-the-art episodic control-based approaches.Our data and code are available at the project website.

Scopus

Publisher：Proceedings 2023 38th IEEE ACM International Conference on Automated Software Engineering Ase 2023  

The reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging. In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different search spaces, ensuring the practicality and validity of test cases. Then, we propose a termination state novelty-based guidance to diversify agent behaviors and improve the test effectiveness. Finally, we evaluate the framework on five widely used benchmarks, including autonomous driving, aircraft collision avoidance, and gaming scenarios. The results demonstrate that our approach identifies more diverse and influential failure-triggering test cases compared to current state-of-the-art techniques. Moreover, we employ the detected failure cases to repair the evaluated models, achieving better robustness enhancement compared to the baseline method.

DOI： 10.1109/ASE56229.2023.00153

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Publisher：Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition  

In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFake-generated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images in an adversarial way. Second, we find that the statistical differences between natural and DeepFake images are positively associated with the distribution shifting between the two kinds of images, and we propose to use a distribution-aware loss to guide the optimization of different degradations. As a result, the feature distributions of generated adversarial examples is close to the natural images. Furthermore, we extend the StatAttack to a more powerful version, MStatAttack, where we extend the single-layer degradation to multi-layer degradations sequentially and use the loss to tune the combination weights jointly. Comprehensive experimental results on four spatial-based detectors and two frequency-based detectors with four datasets demonstrate the effectiveness of our proposed attack method in both white-box and black-box settings.

DOI： 10.1109/CVPR52729.2023.01181

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Publisher：Proceedings 2023 38th IEEE ACM International Conference on Automated Software Engineering Ase 2023  

Quantum Intermediate Representation (QIR) is an LLVM-based intermediate representation developed by Microsoft for quantum program compilers. QIR is designed to offer a universal solution for quantum program compilers, decoupled from both front-end languages and back-end hardware, thereby eliminating the need for redundant development of intermediate representations and compilers. However, the lack of a formal definition and reliance on natural language descriptions in the current state of QIR result in interpretational ambiguity and a dearth of rigor in implementing quantum functions. In this paper, we present formal definitions for QIR's data types and instruction sets to establish correctness and safety assurances for operations and intermediate code conversions within QIR. To demonstrate the effectiveness of our approach, we provide examples of unsafe QIR codes where errors can be identified with our method.

DOI： 10.1109/ASE56229.2023.00195

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Publisher：Esec Fse 2023 Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering  

Deep neural networks (DNNs) achieved a significant performance breakthrough over the past decade and have been widely adopted in various industrial domains. However, a fundamental problem regarding DNN robustness is still not adequately addressed, which can potentially lead to many quality issues after deployment, e.g., safety, security, and reliability. An adversarial attack is one of the most commonly investigated techniques to penetrate a DNN by misleading the DNN's decision through the generation of minor perturbations in the original inputs. More importantly, the adversarial attack is a crucial way to assess, estimate, and understand the robustness boundary of a DNN. Intuitively, a stronger adversarial attack can help obtain a tighter robustness boundary, allowing us to understand the potential worst-case scenario when a DNN is deployed. To push this further, in this paper, we propose DeepRover, a fuzzing-based blackbox attack for deep neural networks used for image classification. We show that DeepRover is more effective and query-efficient in generating adversarial examples than state-of-the-art blackbox attacks. Moreover, DeepRover can find adversarial examples at a finer-grained level than other approaches.

DOI： 10.1145/3611643.3616370

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Publisher：Proceedings 2023 IEEE International Conference on Quantum Software Qsw 2023  

Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing interest in quantum machine learning (QML), ensuring the correctness and robustness of software platforms to develop such QML programs is critical. A necessary step for ensuring the reliability of such platforms is to understand the bugs they typically suffer from. To address this need, this paper presents the first comprehensive study of bugs in QML frameworks. We inspect 391 real-world bugs collected from 22 open-source repositories of nine popular QML frameworks. We find that 1) 28% of the bugs are quantum-specific, such as erroneous unitary matrix implementation, calling for dedicated approaches to find and prevent them; 2) We manually distilled a taxonomy of five symptoms and nine root cause of bugs in QML platforms; 3) We summarized four critical challenges for QML framework developers. The study results provide researchers with insights into how to ensure QML framework quality and present several actionable suggestions for QML framework developers to improve their code quality.

DOI： 10.1109/QSW59989.2023.00018

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Publisher：Empirical Software Engineering  

Recent advances in deploying deep learning (DL) models have inspired the innovation of DL compilers from both industry and academia such as Facebook Glow and TVM. Given the importance of DL compilers, we seek for answering the important question to ease the adoption and development of TVM: What challenges do users face when using DL compilers and what are common challenges for developers when developing DL compilers. This paper presents the first empirical study on identifying the challenges in both usage and development of a DL compiler. We choose TVM as the representative DL compiler and manually inspect 347 sampled posts from its official discuss forum. We identify a taxonomy of challenges in usage of TVM consisting of 15 categories and seven types of common topics about developing TVM. Furthermore, we characterize TVM bugs in total of four impacts to obtain an initial understanding on defects of TVM through manual inspection of 44 bug reports and propose five implications for both developers and researchers in order to improve the development practices and build more robust DL compilers.

DOI： 10.1007/s10664-022-10221-7

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Publisher：IEEE Transactions on Reliability  

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise, etc. Hence, it poses a rather important problem for the DNNs' real-world applications: how to repair the deployed DNNs for correcting the failure samples under the deployed operational environment while not harming their capability of handling normal or clean data with limited failure samples we can collect. In this article, we propose a style-guided data augmentation for repairing DNN in the operational environment, which learns and introduces the unknown failure patterns within the failure samples into the training data via the style transfer. Moreover, we further propose the clustering-based failure data generation for much more effective style-guided data augmentation. We conduct a large-scale evaluation with 15 degradation factors that may happen in the real world and compare with four state-of-the-art data augmentation methods and two DNN repairing methods. Our technique successfully repairs three convolutional neural networks and two recurrent neural networks with averaging 62.88% and 39.02% accuracy enhancements on the 15 failure patterns, respectively, achieving higher repairing performance than state-of-the-art repairing methods on the most failure patterns with even better accuracy on clean datasets.

DOI： 10.1109/TR.2021.3096332

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Publisher：Pattern Recognition  

Network architecture search (NAS), in particular the differentiable architecture search (DARTS) method, has shown a great power to learn excellent model architectures on the specific dataset of interest. In contrast to using a fixed dataset, in this work, we focus on a different but important scenario for NAS: how to refine a deployed network's model architecture to enhance its robustness with the guidance of a few collected and misclassified examples that are degraded by some real-world unknown corruptions having a specific pattern (e.g., noise, blur, etc.). To this end, we first conduct an empirical study to validate that the model architectures can be definitely related to the corruption patterns. Surprisingly, by just adding a few corrupted and misclassified examples (e.g., 10³ examples) to the clean training dataset (e.g., 5.0×10⁴ examples), we can refine the model architecture and enhance the robustness significantly. To make it more practical, the key problem, i.e., how to select the proper failure examples for the effective NAS guidance, should be carefully investigated. Then, we propose a novel core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture. We use our method for DARTS-refined DNNs on the clean as well as 15 corruptions with the guidance of four specific real-world corruptions. Compared with the state-of-the-art NAS as well as data-augmentation-based enhancement methods, our final method can achieve higher accuracy on both corrupted datasets and the original clean dataset. On some of the corruption patterns, we can achieve as high as over 45% absolute accuracy improvements.

DOI： 10.1016/j.patcog.2022.108864

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Publisher：Journal of Systems and Software  

Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose VULPEDIA, which mines expressive vulnerability signatures from contracts. VULPEDIA is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate VULPEDIA with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that VULPEDIA achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.

DOI： 10.1016/j.jss.2022.111410

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Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (other academic)  

Publisher：Proceedings 2022 IEEE 22nd International Conference on Software Quality Reliability and Security Companion Qrs C 2022  

Probabilistic programming systems allow developers to model random phenomena and perform reasoning about the model efficiently. As the number of probabilistic programming systems is growing significantly and are used more and more widely, the reliability of such systems is becoming very important. It is crucial to analyze real bugs of existing similar systems in order to develop efficient bug detection tools for probabilistic programming systems. This paper conducts an empirical study investigating bugs and their features on PyMC3, a real probabilistic programming system. Among 271 closed bugs, we identified 20 bugs that are unique to probabilistic programming languages and extracted eight bug patterns from these bugs. The result showed that many of the bugs were caused by types. We also propose some possible methods for automatically detecting these bug patterns. It is expected that this will contribute to the development of bug detection tools by capturing the characteristics of bugs in actual probabilistic programs in the future.

DOI： 10.1109/QRS-C57518.2022.00063

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Publisher：IEEE International Conference on Software Quality Reliability and Security Qrs  

While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for software developers unfamiliar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing through a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.

DOI： 10.1109/QRS57517.2022.00075

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Publisher：Proceedings 2022 IEEE International Conference on Software Analysis Evolution and Reengineering Saner 2022  

As quantum programming evolves, more and more quantum programming languages are being developed. As a result, debugging and testing quantum programs have become increasingly important. While bug fixing in classical programs has come a long way, there is a lack of research in quantum programs. To this end, this paper presents a comprehensive study on bug fixing in quantum programs. We collect and investigate 96 real-world bugs and their fixes from four popular quantum programming languages (Qiskit, Cirq, Q#, and ProjectQ). Our study shows that a high proportion of bugs in quantum programs are quantum-specific bugs (over 80%), which requires further research in the bug fixing domain. We also summarize and extend the bug patterns in quantum programs and subdivide the most critical part, math-related bugs, to make it more applicable to the study of quantum programs. Our findings summarize the characteristics of bugs in quantum programs and provide a basis for studying testing and debugging quantum programs.

DOI： 10.1109/SANER53432.2022.00147

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Language：English   Publishing type：Research paper (other academic)  

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DOI： 10.1109/QRS-C5114.2020.00026

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AngularJS is a popular framework for single page web applications. Due to separation of programming logic and GUI, the data and control flow in AngularJS applications are usually hard to track. We propose a white-box method for first integrating the separate concerns into one interaction diagram, which contains the overall data and control flow of a program, and then separating user interactions from each other. With the help of the interactions, our method helps to achieve a better understanding of AgnularJS-based single page web applications, and moreover provides novel test coverage criteria for them.

DOI： 10.1007/978-3-030-51253-8_10

Language：English   Publishing type：Research paper (other academic)  

Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework-DeepStellar-to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We demonstrated that, with DeepStellar, users are able to design efficient test generation tools, and develop effective adversarial sample detectors. We tested the developed applications on three real RNN models, including speech recognition and image classification. DeepStellar outperforms existing approaches three hundred times in generating defect-triggering tests and achieves 97% accuracy in detecting adversarial attacks. A video demonstration which shows the main features of DeepStellar is available at: https://sites.google.com/view/deepstellar/tool-demo.

DOI： 10.1109/ASE.2019.00102

Language：English   Publishing type：Research paper (other academic)  

Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables to statically analyze the robustness of a DNN model against the input as a whole, but also allows to identify the vulnerable segments of a sequential input (e.g. audio input) by runtime analysis. It is worth noting that DeepMutation++ specially features the support of RNNs mutation testing. The tool demo video can be found on the project website https://sites.google.com/view/deepmutationpp.

DOI： 10.1109/ASE.2019.00126

Language：English   Publishing type：Research paper (other academic)  

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support multiple built-in configurations which are easy to extend. We evaluated DeepHunter on two popular datasets and the results demonstrate the effectiveness of DeepHunter in achieving coverage increase and detecting real defects. A video demonstration which showcases the main features of DeepHunter can be found at https://youtu.be/s5DfLErcgrc.

DOI： 10.1109/ASE.2019.00127

Language：English   Publishing type：Research paper (other academic)  

Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice. To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks and platforms by using two types of DNN architectures and three popular datasets. (1) For development process, we investigate the prediction accuracy under the same runtime training configuration or same model weights/biases. We also study the adversarial robustness of trained models by leveraging the existing adversarial attack techniques. The experimental results show that the computing differences across frameworks could result in an obvious prediction accuracy decline, which should draw the attention of DL developers. (2) For deployment process, we investigate the prediction accuracy and performance (refers to time cost and memory consumption) when the trained models are migrated/quantized from PC to real mobile devices and web browsers. The DL platform study unveils that the migration and quantization still suffer from compatibility and reliability issues. Meanwhile, we find several DL software bugs by using the results as a benchmark. We further validate the results through bug confirmation from stakeholders and industrial positive feedback to highlight the implications of our study. Through our study, we summarize practical guidelines, identify challenges and pinpoint new research directions, such as understanding the characteristics of DL frameworks and platforms, avoiding compatibility and reliability issues, detecting DL software bugs, and reducing time cost and memory consumption towards developing and deploying high quality DL systems effectively.

DOI： 10.1109/ASE.2019.00080

Language：English   Publishing type：Research paper (other academic)  

Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two trace similarity metrics and five coverage criteria which enable the quantitative analysis of RNNs. We further propose two algorithms powered by the quantitative measures for adversarial sample detection and coverage-guided test generation. We evaluate DeepStellar on four RNN-based systems covering image classification and automated speech recognition. The results demonstrate that the abstract model is useful in capturing the internal behaviors of RNNs, and confirm that (1) the similarity metrics could effectively capture the differences between samples even with very small perturbations (achieving 97% accuracy for detecting adversarial samples) and (2) the coverage criteria are useful in revealing erroneous behaviors (generating three times more adversarial samples than random testing and hundreds times more than the unrolling approach).

DOI： 10.1145/3338906.3338954

Language：English   Publishing type：Research paper (other academic)  

The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a seed selection strategy that combines both diversity-based and recency-based seed selection. We implement and incorporate 5 existing testing criteria and 4 seed selection strategies in DeepHunter. Large-scale experiments demonstrate that (1) our metamorphic mutation strategy is useful to generate new valid tests with the same semantics as the original seed, by up to a 98% validity ratio; (2) the diversity-based seed selection generally weighs more than recency-based seed selection in boosting the coverage and in detecting defects; (3) DeepHunter outperforms the state of the arts by coverage as well as the quantity and diversity of defects identified; (4) guided by corner-region based criteria, DeepHunter is useful to capture defects during the DNN quantization for platform migration.

DOI： 10.1145/3293882.3330579

Language：English   Publishing type：Research paper (other academic)  

As deep learning (DL) opens the way to many technological innovations in a wild range of fields, more and more researchers and developers from diverse domains start to take advantage of DLs. In many circumstances, a developer leverages a DL framework and programs the training software in the form of source code (e.g., Python, Java). However, not all of the developers across domains are skilled at programming. It is highly desirable to provide a way so that a developer could focus on how to design and optimize their DL systems instead of spending too much time on programming. To simplify the programming process towards saving time and effort especially for beginners, we propose and implement DeepVisual, a visual programming tool for the design and development of DL systems. DeepVisual represents each layer of a neural network as a component. A user can drag-and-drop components to design and build a DL model, after which the training code is automatically generated. Moreover, DeepVisual supports to extract the neural network architecture on the given source code as input. We implement DeepVisual as a PyCharm plugin and demonstrate its usefulness on two typical use cases.

DOI： 10.1109/ICPC.2019.00028

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ICSTW.2019.00021

Language：English   Publishing type：Research paper (scientific journal)  

Context: Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework. Objective: This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) functional APIs for implementing a functionality and (2) the event callback APIs that are to be overridden to contain the functional code. Method: We carry out an empirical study on actual Android programming questions from StackOverflow to confirm the need of recommending callbacks. Then we build Android-specific API databases to contain the correlations among various functionalities and APIs, based on customized parsing of code snippets and natural language processing of texts in Android tutorials and SDK documents, and then textual and code similarity metrics are adapted for recommending relevant APIs. Results: We have evaluated our prototype recommendation engine, named LibraryGuru, with about 1500 questions on Android programming from StackOverflow, and demonstrated that our top-5 results on recommending callbacks and functional APIs can on estimate achieve up to 43.5% and 50.9% respectively in precision, 24.6% and 32.5% respectively in mean average precision (MAP) scores, and 51.1% and 44.0% respectively in recall. Conclusion: We conclude that it is important and possible to recommend both functional APIs and callbacks for Android application development, and future work is needed to take more data sources into consideration to make more relevant recommendations for developers’ needs.

DOI： 10.1016/j.infsof.2018.10.010

Language：English   Publishing type：Research paper (other academic)  

Deep learning (DL) has achieved remarkable progress over the past decade and has been widely applied to many industry domains. However, the robustness of DL systems recently becomes great concerns, where minor perturbation on the input might cause the DL malfunction. These robustness issues could potentially result in severe consequences when a DL system is deployed to safety-critical applications and hinder the real-world deployment of DL systems. Testing techniques enable the robustness evaluation and vulnerable issue detection of a DL system at an early stage. The main challenge of testing a DL system attributes to the high dimensionality of its inputs and large internal latent feature space, which makes testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to balance the testing exploration effort and defect detection capabilities. In this paper, we perform an exploratory study of CT on DL systems. We propose a set of combinatorial testing criteria specialized for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems.

DOI： 10.1109/SANER.2019.8668044

Language：English   Publishing type：Research paper (other academic)  

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics. Deep neural networks are the key driving force behind its recent success, but still seem to be a magic black box lacking interpretability and understanding. This brings up many open safety and security issues with enormous and urgent demands on rigorous methodologies and engineering practice for quality enhancement. A plethora of studies have shown that state-of-the-art DL systems suffer from defects and vulnerabilities that can lead to severe loss and tragedies, especially when applied to real-world safety-critical applications. In this paper, we perform a large-scale study and construct a paper repository of 223 relevant works to the quality assurance, security, and interpretation of deep learning. Based on this, we, from a software quality assurance perspective, pinpoint challenges and future opportunities to facilitate drawing the attention of the software engineering community towards addressing the pressing industrial demand of secure intelligent systems.

DOI： 10.1007/978-3-030-32409-4_1

Language：English   Publishing type：Research paper (other academic)  

As the software systems are becoming more and more complicated, developers have an increasing dependency on code recommendation tools to assist them to fulfill their development tasks. However, the current historical-code-based recommendation methods are directly affected by the quality of the historical codes and the program-environment-information-based recommendation methods cannot provide satisfactory recommendation results for static methods because it is difficult to know all possible static members only using the program context, and even if we know all the static members, we still cannot add all of them to the entry point for search because its large number may cause a space explosion. In this paper, we propose a type conversion sequence recommendation method based on program environment information. Combing with the reachability analysis using semantic Web technology, the proposed method tries to reduce the searching entry points to solve the space explosion problem caused by the recommendation of static methods. We implemented an Eclipse plug-in based on the proposed method and conducted experiments on Tomcat source code. The experimental results showed that the proposed method can not only recommend type conversion sequences with static methods effectively, but also has a higher accuracy for the recommendation of object methods compared with the Eclipse Code Recommenders.

DOI： 10.1109/SmartWorld.2018.00076

Language：English   Publishing type：Research paper (other academic)  

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.

DOI： 10.1109/ISSRE.2018.00021

Language：English   Publishing type：Research paper (other academic)  

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.

DOI： 10.1145/3238147.3238202

Language：English   Publishing type：Research paper (scientific journal)  

Model checking is a formal verification technique. It takes an exhaustively strategy to check hardware circuits and network protocols against desired properties. Having been developed for more than three decades, model checking is now playing an important role in software engineering for verifying rather complicated software artifacts. This paper surveys the role of model checking in software engineering. In particular, we searched for the related literatures published at reputed conferences, symposiums, workshops, and journals, and took a survey of (1) various model checking techniques that can be adapted to software development and their implementations, and (2) the use of model checking at different stages of a software development life cycle. We observed that model checking is useful for software debugging, constraint solving, and malware detection, and it can help verify different types of software systems, such as object- and aspect-oriented systems, service-oriented applications, web-based applications, and GUI applications including safety- and mission-critical systems. The survey is expected to help human engineers understand the role of model checking in software engineering, and as well decide which model checking technique(s) and/or tool(s) are applicable for developing, analyzing and verifying a practical software system. For researchers, the survey also points out how model checking has been adapted to their research topics on software engineering and its challenges.

DOI： 10.1007/s11704-016-6192-0

Language：English   Publishing type：Research paper (other academic)  

As more and more domain specific big data become available, there comes a strong need on the fast development and deployment of deep learning (DL) systems with high quality for domain specific applications, including many safety-critical scenarios. In traditional software engineering, software visualization plays an important role to enhance developers' performance with many tools available. However, there are limited visualization supports existing for DL systems, especially in integrated development environments (IDEs) that allow a developer to visualize the source code of a deep neural network (DNN) and its graph architecture. In this paper, we propose DeepGraph, a visualization tool for visualizing and understanding a deep neural network. DeepGraph analyzes the training program to construct the graph representation of a DNN, and establishes and maintains the linkage (mapping) between the source code of the training program and its corresponding neural network architecture. We implemented DeepGraph as a PyCharm plugin and performed preliminary empirical study to demonstrate its usefulness for understanding deep nueral networks.

DOI： 10.1109/APSEC.2018.00079

Language：English   Publishing type：Research paper (scientific journal)  

Symbolic execution is widely used in many code analysis, testing, and verification tools. As symbolic execution exhaustively explores all feasible paths, it is quite time consuming. To handle the problem, researchers have paralleled existing symbolic execution tools (e.g., KLEE). In particular, Cloud9 is a widely used paralleled symbolic execution tool, and researchers have used the tool to analyze real code. However, researchers criticize that tools such as Cloud9 still cannot analyze large scale code. In this paper, we conduct a field study on Cloud9, in which we use KLEE and Cloud9 to analyze benchmarks in C. Our results confirm the criticism. Based on the results, we identify three bottlenecks that hinder the performance of Cloud9: the communication time gap, the job transfer policy, and the cache management of the solved constraints. To handle these problems, we tune the communication time gap with better parameters, modify the job transfer policy, and implement an approach for cache management of solved constraints. We conduct two evaluations on our benchmarks and a real application to understand our improvements. Our results show that our tuned Cloud9 reduces the execution time significantly, both on our benchmarks and the real application. Furthermore, our evaluation results show that our tuning techniques improve the effectiveness on all the devices, and the improvement can be achieved upto five times, depending upon a tuning value of our approach and the behaviour of program under test.

DOI： 10.1007/s11704-016-5459-9

Language：English   Publishing type：Research paper (other academic)  

AngularJS is a popular framework for single page web applications. In AngularJS applications, the pro-gramming logic is implemented in Javascript, while the layout is defined separately in HTML files. Due to this separation, data and control ow is usually hard to track. We propose a method to visualize the data and control flow in AngularJS-based single page web applications and separate interactions from each other. Our method helps to get a better understanding of the application's work flow, to realize the boundaries of the interactions, and to know what is updated in an interaction and what is not.

DOI： 10.18293/SEKE2018-066

Language：English   Publishing type：Research paper (scientific journal)  

When debugging bugs, programmers often prepare test cases to reproduce buggy behaviours. However, for concurrent programs, test cases alone are typically insufficient to reproduce buggy behaviours, due to the nondeterminism of multi-threaded executions. In literature, various approaches have been proposed to reproduce buggy behaviours for concurrency bugs deterministically, but to the best of our knowledge, they are still limited. In particular, we have recognized three debugging scenarios from programming practice, but existing approaches can handle only one of the scenarios. In this paper, we propose a novel approach, called SPDebugger, that provides finer-grained thread controlling over test cases, programs under test, and even third party library code, to reproduce the predesigned thread execution schedule. The evaluation shows that SPDebugger handles more debugging scenarios than the state-of-the-art tool, called IMUnit, with similar human effort.

DOI： 10.1587/transinf.2016EDP7388

Language：English   Publishing type：Research paper (scientific journal)  

The proliferation of diverse kinds of programming languages and platforms makes it a common need to have the same functionality implemented in different languages for different platforms, such as Java for Android applications and C# forWindows phone applications. Although versions of code written in different languages appear syntactically quite different from each other, they are intended to implement the same software and typically contain many code snippets that implement similar functionalities, which we call cross-language clones. When the version of code in one language evolves according to changing functionality requirements and/or bug fixes, its cross-language clones may also need be changed to maintain consistent implementations for the same functionality. Thus, it is needed to have automated ways to locate and track cross-language clones within the evolving software. In the literature, approaches for detecting cross-language clones are only for languages that share a common intermediate language (such as the .NET language family) because they are built on techniques for detecting single-language clones. To extend the capability of cross-language clone detection to more diverse kinds of languages, we propose a novel automated approach, CLCMiner, without the need of an intermediate language. It mines such clones from revision histories, based on our assumption that revisions to different versions of code implemented in different languages may naturally reflect how programmers change cross-language clones in practice, and that similarities among the revisions (referred to as clones in diffs or diff clones) may indicate actual similar code. We have implemented a prototype and applied it to ten open source projects implementations in both Java and C#. The reported clones that occur in revision histories are of high precisions (89% on average) and recalls (95% on average). Compared with token-based code clone detection tools that can treat code as plain texts, our tool can detect significantly more cross-language clones. All the evaluation results demonstrate the feasibility of revision-history based techniques for detecting cross-language clones without intermediates and point to promising future work.

DOI： 10.1587/transinf.2016EDP7334

Language：English   Publishing type：Research paper (other academic)  

More and more mobile applications run on multiple mobile operating systems to attract more users of different platforms. Although versions on different platforms are implemented in different programming languages (e.g., Java and Objective-C), there must be many code snippets that implement the similar business logic on different platforms. Such code snippets are called cross-platform clones. It is challenging but essential to detect such clones for software maintenance. Due to the practice that developers usually use some common identifiers when implementing the same business logic on different platforms, in this paper, we investigate the identifier similarity of the same mobile application on different platforms and provide insights about the feasibility of cross-platform clone detection via identifier similarity. In our experiment, we have analyzed the source code of 18 open-source cross-platform applications which are implemented on Android, iOS and Windows Phone, and find that the smaller KL-Divergence the application has, the more accurate the clones detected by identifiers will be.

DOI： 10.1145/2975961.2975967

Language：English   Publishing type：Research paper (other academic)  

Detecting lock-related defects has long been a hot research topic in software engineering. Many efforts have been spent on detecting such deadlocks in concurrent software systems. However, latent locks may be hidden in application programming interface (API) methods whose source code may not be accessible to developers. Many APIs have latent locks. For example, our study has shown that J2SE alone can have 2,000+ latent locks. As latent locks are less known by developers, they can cause deadlocks that are hard to perceive or diagnose. Meanwhile, the state-of-the-art tools mostly handle API methods as black boxes, and cannot detect deadlocks that involve such latent locks. In this paper, we propose a novel black-box testing approach, called LockPeeker, that reveals latent locks in Java APIs. The essential idea of LockPeeker is that latent locks of a given API method can be revealed by testing the method and summarizing the locking effects during testing execution. We have evaluated LockPeeker on ten real-world Java projects. Our evaluation results show that (1) LockPeeker detects 74.9% of latent locks in API methods, and (2) it enables state-of-the-art tools to detect deadlocks that otherwise cannot be detected.

DOI： 10.1145/2970276.2970355

Language：English   Publishing type：Research paper (other academic)  

To attract more users on different platforms, many projects release their versions in multiple programming languages (e.g., Java and C#). They typically have many code snippets that implement similar functionalities, i.e., cross-language clones. Programmers often need to track and modify crosslanguage clones consistently to maintain similar functionalities across different language implementations. In literature, researchers have proposed approaches to detect crosslanguage clones, mostly for languages that share a common intermediate language (such as the .NET language family) so that techniques for detecting single-language clones can be applied. As a result, those approaches cannot detect cross-language clones for many projects that are not implemented in a .NET language. To overcome the limitation, in this paper, we propose a novel approach, CLCMiner, that detects cross-language clones automatically without the need of an intermediate language. Our approach mines such clones from revision histories, which reect how programmers maintain cross-language clones in practice. We have implemented a prototype tool for our approach and conducted an evaluation on five open source projects that have versions in Java and C#. The results show that CLCMiner achieves high accuracy and point to promising future work.

DOI： 10.1145/2970276.2970363

Language：English   Publishing type：Research paper (other academic)  

Test cases are useful for program comprehension. Developers often understand dynamic behavior of systems by running their test cases. As manual testing is expensive, automatic testing has been extensively studied to reduce the cost. However, without sufficient knowledge of the software under test, it is difficult for automated testing techniques to create effective test cases, especially for software that requires complex inputs. In this paper, we propose to reuse existing test cases from the libraries of software under test, to generate better test cases. We have the observation that, when developers start to test the target software, the test cases of its dependent libraries are often available. Therefore, we propose to perform program analysis on these artifacts to extract relevant code fragments to create test sequences. We further seed these sequences to a random test generator GRT to generate test cases for target software. The preliminary experiments show that the technique significantly improves the effectiveness of GRT. Our in-depth analysis reveals that several dependency metrics are good indicators of the potential benefits of applying our technique on specific programs and their libraries.

DOI： 10.1109/ICPC.2016.7503725

Language：English   Publishing type：Research paper (other academic)  

Code clones are prevalent in software systems due to many factors in software development. Detecting code clones and managing consistency between them along code evolution can be very useful for reducing clone-related bugs and maintenance costs. Despite some early attempts at detecting code clones and managing the consistency between them, the state-of-the-art tool can only handle simple code clones whose structures are identical or quite similar. However, existing empirical studies show that clones can have quite different structures with their evolution, which can easily go beyond the capability of the state-of-the-art tool. In this paper, we propose CCSync, a novel, rule-directed approach, which paves the structure differences between the code clones and synchronizes them even when code clones become quite different in their structures. The key steps of this approach are, given two code clones, to (1) extract a synchronization rule from the relationship between the clones, and (2) once one code fragment is updated, propagate the modifications to the other following the synchronization rule. We have implemented a tool for CCSync and evaluated its effectiveness on five Java projects. Our results shows that there are many code clones suitable for synchronization, and our tool achieves precisions of up to 92% and recalls of up to 84%. In particular, more than 76% of our generated revisions are identical with manual revisions.

DOI： 10.1109/ICPC.2016.7503722

Language：English   Publishing type：Research paper (other academic)  

Java virtual machine (JVM) is a core technology, whose reliability is critical. Testing JVM implementations requires painstaking effort in designing test classfiles (∗.class) along with their test oracles. An alternative is to employ binary fuzzing to differentially test JVMs by blindly mutating seeding classfiles and then executing the resulting mutants on different JVM binaries for revealing inconsistent behaviors. However, this blind approach is not cost effective in practice because most of the mutants are invalid and redundant. This paper tackles this challenge by introducing classfuzz, a coverage-directed fuzzing approach that focuses on representative classfiles for differential testing of JVMs' startup processes. Our core insight is to (1) mutate seeding classfiles using a set of predefined mutation operators (mutators) and employ Markov Chain Monte Carlo (MCMC) sampling to guide mutator selection, and (2) execute the mutants on a reference JVM implementation and use coverage uniqueness as a discipline for accepting representative ones. The accepted classfiles are used as inputs to differentially test different JVM implementations and find defects. We have implemented classfuzz and conducted an extensive evaluation of it against existing fuzz testing algorithms. Our evaluation results show that classfuzz can enhance the ratio of discrepancy-triggering classfiles from 1:7% to 11:9%. We have also reported 62 JVM discrepancies, along with the test classfiles, to JVM developers. Many of our reported issues have already been confirmed as JVM defects, and some even match recent clarifications and changes to the Java SE 8 edition of the JVM specification.

DOI： 10.1145/2908080.2908095

Language：English   Publishing type：Research paper (other academic)  

Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search. We deploy the backend of CodeHow as a Microsoft Azure service and implement the front-end as a Visual Studio extension. We evaluate CodeHow on a large-scale codebase consisting of 26K C# projects downloaded from GitHub. The experimental results show that when the top 1 results are inspected, CodeHow achieves a precision score of 0.794 (i.e., 79.4% of the first returned results are relevant code snippets). The results also show that CodeHow outperforms conventional code search tools. Furthermore, we perform a controlled experiment and a survey of Microsoft developers. The results confirm the usefulness and effectiveness of CodeHow in programming practices.

DOI： 10.1109/ASE.2015.42

Language：English   Publishing type：Research paper (other academic)  

Researchers have proposed various approaches to detect concurrency bugs and improve multi-threaded programs, but performing evaluations of the effectiveness of these approaches still remains a substantial challenge. We survey the existing evaluations and find out that they often use code or bugs not representative of real world. To improve representativeness, we have prepared JaConTeBe, a benchmark suite of 47 confirmed concurrency bugs from 8 popular open-source projects, supplemented with test cases for reproducing buggy behaviors. Running three approaches on JaConTeBe shows that our benchmark suite confirms some limitations of the three approaches. We submitted JaConTeBe to the SIR repository (a software-artifact repository for rigorous controlled experiments), and it was included as a part of SIR.

DOI： 10.1109/ASE.2015.87

Language：English   Publishing type：Research paper (scientific journal)  

Predictive trace analysis (PTA), a static trace analysis technique for concurrent programs, can offer powerful capability support for finding concurrency errors unseen in a previous program execution. Existing PTA techniques always face considerable challenges in scaling to large traces which contain numerous critical events. One main reason is that an analyzed trace includes not only redundant memory accessing events and threads that cannot contribute to discovering any additional errors different from the found candidate ones, but also many residual synchronization events which still affect PTA to check whether these candidate ones are feasible or not even after removing the redundant events. Removing them from the trace can significantly improve the scalability of PTA without affecting the quality of the PTA results. In this paper, we propose a biphasic trace filter approach, BIFER in short, to filter these redundant events and residual events for improving the scalability of PTA to expose general concurrency errors. In addition, we design a model which indicates the lock history and the happens-before history of each thread with two kinds of ways to achieve the efficient filtering. We implement a prototypical tool BIFER for Java programs on the basis of a predictive trace analysis framework. Experiments show that BIFER can improve the scalability of PTA during the process of analyzing all of the traces.

DOI： 10.1007/s11704-015-4334-4

Language：English   Publishing type：Research paper (other academic)  

Advanced-dispatching programming languages allow to implicitly alter the behaviour of a program, depending on runtime program context. While this improves modularity, it also impedes comprehensibility. The use of advanced-dispatching programming languages can give rise to complex debugging scenarios, which cannot efficiently be resolved with traditional debugging approaches such as breakpoint-based debugging. Therefore, tool support for analysing the full history of runtime behaviour of advanced-dispatching programs is of importance for efficient debugging. In this paper, we characterise debugging scenarios for which existing work does not apply well, because they require access to the program execution history for efficient resolution. We present our design and implementation of a trace-based debugger for advanced-dispatching that supports such debugging scenarios efficiently. Our approach is the first one based on an XML-representation of the execution trace, giving rise to using very powerful standard tools such as the XQuery language for searching and navigating the trace, or scalable visualisations such as tree-maps.

DOI： 10.1145/2843915.2843922

Language：English   Publishing type：Research paper (scientific journal)  

Program differencing is a widely used technique for program debugging, while it is still not easily used for parallel programs. One main reason is that a parallel program can be complex, and some mechanisms (e.g., place, activity, clock, and barrier) also set barriers for program differencing. In this paper we focus on program differencing for X10 parallel programming language, and design an algorithm for differencing of X10 programs. The algorithm contains three steps: (1) match the places, classes, interfaces, methods, and places between programs of two versions; (2) construct the extended program diagrams for the programs and simplify them to simplified diagrams; (3) iteratively unfold and compare the simplified diagrams and identify the differences between the programs.

DOI： 10.3724/SP.J.1016.2015.01082

Language：English   Publishing type：Research paper (scientific journal)  

Concurrency bugs do significantly affect system reliability. Although many efforts have been made to address this problem, there are still many bugs that cannot be detected because of the complexity of concurrent programs. Compared with atomicity violations, order violations are always neglected. Efficient and effective approaches to detecting order violations are therefore in urgent need. This paper presents a bidirectional predictive trace analysis approach, BIPED, which can detect order violations in parallel based on a recorded program execution. BIPED collects an expected-order execution trace into a layered bidirectional prediction model, which intensively represents two types of expected-order data flows in the bottom layer and combines the lock sets and the bidirectionally order constraints in the upper layer. BIPED then recognizes two types of candidate violation intervals driven by the bottom-layer model and then checks these recognized intervals bidirectionally based on the upper-layer constraint model. Consequently, concrete schedules can be generated to expose order violation bugs. Our experimental results show that BIPED can effectively detect real order violation bugs and the analysis speed is 2.3x-10.9x and 1.24x-1.8x relative to the state-of-the-art predictive dynamic analysis approaches and hybrid model based static prediction analysis approaches in terms of order violation bugs.

DOI： 10.1587/transinf.2014EDP7347

Language：English   Publishing type：Research paper (scientific journal)  

Points-to analysis is a static code analysis technique that establishes the relationships between variables of references and allocated objects. A number of points-to analysis algorithms have been proposed for procedural and object-oriented languages like C and Java, while few of them can be used for AspectJ as we know so far. One main reason is that AspectJ is an aspect-oriented language which implements the separation of crosscutting concerns by advices, pointcuts, and inter-type declarations, while a points-to analysis of AspectJ programs may be imprecise because any aspect woven into the base code may change the points-to relations in the program and thus a conservative analysis has to be taken in order to handle the aspects. In this paper, we propose a context-sensitive points-to analysis technique called AJPoints for AspectJ. Similar to the weaving mechanism for AspectJ, AJPoints obtains the constraints and templates on the points-to relations for the base code and the aspects, respectively, but weaves and solves them in an iterative manner in order to cross the boundary between the base code and the aspects. We have implemented AJPoints on abc AspectJ compiler and evaluated it by using twelve AspectJ benchmark programs. The experimental results show that our technique can achieve a high precision about points-to relations in AspectJ programs.

DOI： 10.1007/s11704-013-3106-2

Language：English   Publishing type：Research paper (other academic)  

Recently code transformations or tailored fitness functions are adopted to achieve coverage (structural or logical criterion) driven testing to ensure software reliability. However, some internal threats like negative impacts on underlying search strategies or local maximum exist. So we propose a dynamic symbolic execution (DSE) based framework combined with a path filtering algorithm and a new heuristic path search strategy, i.e., predictive path search, to achieve faster coverage-driven testing with lower testing cost. The empirical experiments (three open source projects and two industrial projects) show that our approach is effective and efficient. For the open source projects w.r.t branch coverage, our approach in average reduces 25.5% generated test cases and 36.3% solved constraints than the traditional DSE-based approach without path filtering. And the presented heuristic strategy, on the same testing budget, improves the branch coverage by 26.4% and 35.4% than some novel search strategies adopted in KLEE and CREST.

DOI： 10.1109/SERE.2014.23

Language：English   Publishing type：Research paper (other academic)  

With the extensive use of client-side JavaScript in web applications, web contents are becoming more dynamic than ever before. This poses significant challenges for search engines, because more web URLs are now embedded or hidden inside JavaScript code and most web crawlers are script-agnostic, significantly reducing the coverage of search engines. We present a hybrid approach that combines static analysis with dynamic execution, overcoming the weakness of a purely static or dynamic approach that either lacks accuracy or suffers from huge execution cost. We also propose to integrate program analysis techniques such as statement coverage and program slicing to improve the performance of URL mining.

DOI： 10.1145/2491411.2494583

Language：English   Publishing type：Research paper (other academic)  

X10 is a HPC (High Performance Computing) programming language proposed by IBMfor supporting a PGAS (Partitioned Global Address Space) programming model offering a shared address space. The address space can be further partitioned into several logical locations where objects and activities (or threads) will be dynamically created. An analysis of locations can help to check the safety of object accesses through exploring which objects and activities may reside in which locations, while in practice the objects and activities are usually designated at runtime and their locations may also vary under different environments. In this paper, we propose a constraint-based locality analysis method called Leopard for X10. Leopard calculates the points-to relations for analyzing the objects and activities in a program and uses a place constraint graph to analyze their locations. We have developed a tool to support Leopard, and conducted an experiment to evaluate its effectiveness and efficiency. The experimental results show that Leopard can calculate the locations of objects and activities precisely.

DOI： 10.1145/2426890.2426915

Language：English   Publishing type：Research paper (other academic)  

Spectrum-based fault localization (S FL) is a lightweight automated diagnosis technique. However, when applied to object-oriented programs, its diagnostic accuracy is limited as suspicious statements are distributed in different classes. In th is paper, we propose an approach to leveraging method call anomalies to improve the effectiveness of SFL techniques for locating faulty statements in an object-oriented program. First, we compute the suspiciousness for each class based on the difference in its method call sequences between passed and failed runs. Then, we use the suspiciousness information of classes to refine SFL ranks in order to enhance their fault localization effectiveness of object-oriented software. The empirical results show that the proposed approach is able to improve the effectiveness of SFL techniques.

DOI： 10.1109/QSIC.2012.30

Language：English   Publishing type：Research paper (other academic)  

Efficient tools are indispensable in the battle against software bugs. In this short paper, we introduce two techniques that target different phases of an interactive and iterative debugging session. To make slice-assisted log analysis practical to help fault diagnosis, slicing itself must be done instantaneously. We split the costly slicing computation into online and offline, and employ incremental updates after program edits. The result is a vast reduction of slicing cost. For the benchmarks we tested, slices can be computed in the range of seconds, which is 0.02%-6.5% of the unmodified slicing algorithm. The possibility of running slicing in situ and with instant response time gives rise to the possibility of editing-time validation, which we call dryrun. The idea is that a pair of slices, one forward from root cause and one backward from the bug site, defines the scope to validate a fix. This localization makes it possible to invoke symbolic execution and constraint solving that are otherwise too expensive to use in an interactive debugging environment.

DOI： 10.1145/2349896.2349908

Language：English   Publishing type：Research paper (other academic)  

Programmers extensively use application programming interfaces (APIs) to leverage existing libraries and frameworks. However, correctly and efficiently choosing and using APIs from unfamiliar libraries and frameworks is still a non-trivial task. Programmers often need to ruminate on API documentations (that are often incomplete) or inspect code examples (that are often absent) to learn API usage patterns. Recently, various techniques have been proposed to alleviate this problem by creating API summarizations, mining code examples, or showing common API call sequences. However, few techniques focus on recommending API parameters. In this paper, we propose an automated technique, called Precise, to address this problem. Differing from common code completion systems, Precise mines existing code bases, uses an abstract usage instance representation for each API usage example, and then builds a parameter usage database. Upon a request, Precise queries the database for abstract usage instances in similar contexts and generates parameter candidates by concretizing the instances adaptively. The experimental results show that our technique is more general and applicable than existing code completion systems, specially, 64% of the parameter recommendations are useful and 53% of the recommendations are exactly the same as the actual parameters needed. We have also performed a user study to show our technique is useful in practice.

DOI： 10.1109/ICSE.2012.6227136

Language：English   Publishing type：Research paper (other academic)  

As a classical data-flow analysis, reaching definitions analysis is the corner stone of various techniques, such as code optimization and program slicing. The built-in data-flow analysis framework in Soot has been implemented in the traditional iterative style. While being able to meet general requirements for implementation of data flow analyses, the framework may be less efficient for a certain type of analyses in which the complete data-flow solution is unnecessary. In this paper, we introduce our Soot-based implementation of an inter-procedural demand-driven reaching definitions analysis. For a demand for reaching definitions facts, the analysis only explores relevant program points and variables, saving a considerable amount of computation. Preliminary results show that the implementation can be much more efficient than its traditional counterpart in several scenarios. The Soot framework has greatly facilitated the implementation by providing abundant basic analysis results via well designed APIs.

DOI： 10.1145/2259051.2259055

Language：English   Publishing type：Research paper (scientific journal)  

There is a common belief that structural properties of classes are important factors to determine their unit testability. However, few empirical studies have been conducted to examine the actual impact of structural properties of classes. In this paper, we employ multiple linear regression (MLR) and partial least square regression (PLSR) to investigate the relationships between the metrics measuring structural properties and unit testability of a class. The investigated structural metrics cover five property dimensions, including size, cohesion, coupling, inheritance, and complexity. Our results from open-source software systems show that: (1) most structural metrics are statistically related to unit testability in an expected direction, among which size, complexity, and coupling metrics are the most important predictors; that (2) multivariate regression models based on structural metrics cannot accurately predict unit testability of classes, although they are better able to rank unit testability of classes; that (3) the transition from MLR to PLSR could significantly improve the ability to rank unit testability of classes but cannot improve the ability to predict the unit testing effort of classes.

DOI： 10.1007/s11432-012-4745-x

Language：English   Publishing type：Research paper (other academic)  

The information of execution frequencies of virtual call targets is valuable for program analyses and optimizations of object-oriented programs. However, to obtain this information, most of the existing approaches rely on dynamic profiling. They usually require running the programs with representative workloads, which are often absent in practice. Additionally, some kinds of programs are very sensitive to run-time disturbance, thus are generally not suitable for dynamic profiling. Therefore, a technique which can statically estimate the execution frequencies of virtual call targets will be very useful. In this paper we propose an evidence-based approach to frequency estimation of virtual call targets. By applying machine learning algorithms on the data collected from a group of selected programs, our approach builds an estimation model to capture the relations between static features and run-time program behaviors. Then, for a new program, the approach estimates the relative frequency for each virtual call target by applying the model to the static features of the program. Once the model has been built, the estimation step is purely static, thus does not suffer the shortcomings of existing dynamic techniques. We have performed a number of experiments on real-world large-scale programs to evaluate our approach. The results show that our approach can estimate frequency distributions which are much more informative than the commonly used uniform distribution.

DOI： 10.1007/978-3-642-22655-7_24

Language：English   Publishing type：Research paper (other academic)  

Probabilistic points-to analysis is an analysis technique for defining the probabilities on the points-to relations in programs. It provides the compiler with some optimization chances such as speculative dead store elimination, speculative redundancy elimination, and speculative code scheduling. Although several static probabilistic points-to analysis techniques have been developed for C language, they cannot be applied directly to Java because they do not handle the classes, objects, inheritances and invocations of virtual methods. In this paper, we propose a context-insensitive and flow-sensitive probabilistic points-to analysis for Java (JPPA) for statically predicting the probability of points-to relations at all program points (i.e., points before or after statements) of a Java program. JPPA first constructs an interprocedural control flow graph (ICFG) for a Java program, whose edges are labeled with the probabilities calculated by an algorithm based on a static branch prediction approach, and then calculates the probabilistic points-to relations of the program based upon the ICFG. We have also developed a tool called Lukewarm to support JPPA and conducted an experiment to compare JPPA with a traditional context-insensitive and flow-sensitive points-to analysis approach. The experimental results show that JPPA is a precise and effective probabilistic points-to analysis technique for Java.

DOI： 10.1007/978-3-642-19861-8_5

Language：English   Publishing type：Research paper (other academic)  

Logs are valuable for failure diagnosis and software debugging in practice. However, due to the ad-hoc style of inserting logging statements, the quality of logs can hardly be guaranteed. In case of a system failure, the log file may contain a large number of irrelevant logs, while crucial clues to the root cause may still be missing. In this paper, we present an automated approach to log improvement based on the combination of information from program source code and textual logs. It selects the most relevant ones from an ocean of logs to help developers focus and reason along the causality chain, and generates additional informative logs to help developers discover the root causes of failures. We have conducted a preliminary case study using an implementation prototype to demonstrate the usefulness of our approach.

DOI： 10.1145/2103799.2103811

Language：English   Publishing type：Research paper (other academic)  

Static analysis tools have been widely used to detect potential defects without executing programs. It helps programmers raise the awareness about subtle correctness issues in the early stage. However, static defect detection tools face the high false positive rate problem. Therefore, programmers have to spend a considerable amount of time on screening out real bugs from a large number of reported warnings, which is time-consuming and inefficient. To alleviate the above problem during the report inspection process, we present EFindBugs to employ an effective two-stage error ranking strategy that suppresses the false positives and ranks the true error reports on top, so that real bugs existing in the programs could be more easily found and fixed by the programmers. In the first stage, EFindBugs initializes the ranking by assigning predefined defect likelihood for each bug pattern and sorting the error reports by the defect likelihood in descending order. In the second stage, EFindbugs optimizes the initial ranking self-adaptively through the feedback from users. This optimization process is executed automatically and based on the correlations among error reports with the same bug pattern. Our experiment on three widely-used Java projects (AspectJ, Tomcat, and Axis) shows that our ranking strategy outperforms the original ranking in Find Bugs in terms of precision, recall and F1-score.

DOI： 10.1109/ICST.2011.51

Language：English   Publishing type：Research paper (other academic)  

During debugging processes, breakpoints are frequently used to inspect and understand runtime behaviors of programs. Although most development environments offer convenient breakpoint facilities, the use of these environments usually requires considerable human efforts in order to generate useful breakpoints. Before setting breakpoints or typing breakpoint conditions, developers usually have to make some judgements and hypotheses on the basis of their observations and experience. To reduce this kind of efforts we present a tool, named BPGen, to automatically generate breakpoints for debugging. BPGen uses three well-known dynamic fault localization techniques in tandem to identify suspicious program statements and states, through which both conditional and unconditional breakpoints are generated. BPGen is implemented as an Eclipse plugin for supplementing the existing Eclipse JDT debugger.

DOI： 10.1145/1810295.1810351

Language：English   Publishing type：Research paper (other academic)  

Concurrent programs often exhibit bugs due to unintended interferences among the concurrent threads. Such bugs are often hard to reproduce because they typically happen under very specific interleaving of the executing threads. Basically, it is very hard to fix a bug (or software failure) in concurrent programs without being able to reproduce it. In this paper, we present an approach, called ConCrash, that automatically and deterministically reproduces concurrent failures by recording logical thread schedule and generating unit tests. For a given bug (failure), ConCrash records the logical thread scheduling order and preserves object states in memory at runtime. Then, ConCrash reproduces the failure offline by simply using the saved information without the need for JVM-level or OS-level support. To reduce the runtime performance overhead, ConCrash employs a static data race detection technique to report potential possible race conditions, and only instruments such places. We implement the ConCrash approach in a prototype tool for Java and experimented on a number of multi-threaded Java benchmarks. As a result, we successfully reproduced a number of real concurrent bugs (e.g., deadlocks, data races and atomicity violation) within an acceptable overhead.

DOI： 10.1007/978-3-642-12029-9_23

Language：English   Publishing type：Research paper (other academic)  

Adaptive Random Testing (ART) is a testing technique which is based on an observation that a test input usually has the same potential as its neighbors in detection of a specific program defect. ART helps to improve the efficiency of random testing in that test inputs are selected evenly across the input spaces. However, the application of ART to object-oriented programs (e.g., C++ and Java) still faces a strong challenge in that the input spaces of object-oriented programs are usually high dimensional, and therefore an even distribution of test inputs in a space as such is difficult to achieve. In this paper, we propose a divergence-oriented approach to adaptive random testing of Java programs to address this challenge. The essential idea of this approach is to prepare for the tested program a pool of test inputs each of which is of significant difference from the others, and then to use the ART technique to select test inputs from the pool for the tested program. We also develop a tool called ARTGen to support this testing approach, and conduct experiment to test several popular open-source Java packages to assess the effectiveness of the approach. The experimental result shows that our approach can generate test cases with high quality.

DOI： 10.1109/ASE.2009.13

Language：English   Publishing type：Research paper (other academic)  

Program differencing is a common means of software de-bugging. Although many differencing algorithms have been proposed for procedural and object-oriented languages like C and Java, there is no differencing algorithm for aspect-oriented languages so far. In this paper we propose an approach for difference analysis of aspect-oriented programs. The proposed algorithm contains a novel way of matching two versions of a module of which the signature has been modifed. For this, we also work out a set of well defined signatures for the new elements in the AspectJ language. In accordance with these signatures, and with those existent for elements of the Java language, we investigate a set of signature patterns to be used with the module matching algorithm. Furthermore, we demonstrate successful application of a node-by-node comparison algorithm originally developed for object-oriented programs. Using a tool which implements our algorithms, we set up and evaluate a set of test cases. The results demonstrate the effectiveness of our approach for a large subset of the AspectJ language.

DOI： 10.1145/1572272.1572276

Language：English   Publishing type：Research paper (other academic)  

Program call graph representation can be used to support many tasks in compiler optimization, program comprehension, and software maintenance. During software evolution, the call graph needs to remain fairly precise and be updated quickly in response to software changes. In this paper, we present an approach to incremental update, instead of exhaustive analysis of the initially constructed call graph in AspectJ software. Our approach first decomposes the source code edits between the updated and initial software versions into a set of atomic change representations, which capture the semantic differences. Then, we explore the relationship between atomic changes and call graph to incrementally update the initially constructed graph, instead of rebuilding it from the ground up. We implement the reanalysis approach on top of the ajc AspectJ compiler and perform an empirical study on 24 versions of eight AspectJ benchmarks. The experiment result shows that our approach can reduce a large portion of unnecessary reanalysis cost as program changes occur, and significant savings are observed for the incremental reconstruction of AspectJ call graph in comparison with an exhaustive analysis, with no loss in precision.

DOI： 10.1109/ICSM.2009.5306311

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented programming (AOP) is gaining popularity with the wider adoption of languages such as AspectJ. During AspectJ software evolution, when regression tests fail, it may be tedious for programmers to find out the failure-inducing changes by manually inspecting all code editing. To eliminate the expensive effort spent on debugging, we developed AutoFlow, an automatic debugging tool for AspectJ software. AutoFlow integrates the potential of delta debugging algorithm with the benefit of change impact analysis to narrow down the search for faulty changes. It first uses change impact analysis to identify a subset of responsible changes for a failed test, then ranks these changes according to our proposed heuristic (indicating the likelihood that they may have contributed to the failure), and finally employs an improved delta debugging algorithm to determine a minimal set of faulty changes. The main feature of AutoFlow is that it can automatically reduce a large portion of irrelevant changes in an early phase, and then locate faulty changes effectively.

DOI： 10.1109/ICSM.2008.4658109

Language：English   Publishing type：Research paper (scientific journal)  

Test case prioritization techniques have been empirically proved to be effective in improving the rate of fault detection in regression testing. However, most of previous techniques assume that all the faults have equal severity, which dose not meet the practice. In addition, because most of the existing techniques rely on the information gained from previous execution of test cases or source code changes, few of them can be directly applied to non-regression testing. In this paper, aiming to improve the rate of severe faults detection for both regression testing and non-regression testing, we propose a novel test case prioritization approach based on the analysis of program structure. The key idea of our approach is the evaluation of testing-importance for each module (e.g., method) covered by test cases. As a proof of concept, we implement Apros, a test case prioritization tool, and perform an empirical study on two real, non-trivial Java programs. The experimental result represents that our approach could be a promising solution to improve the rate of severe faults detection.

DOI： 10.1109/APSEC.2008.63

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented software development (AOSD) is gaining popularity with the wider adoption of languages such as AspectJ. However, though the state-of-the-art aspect-oriented programming environment (such as AJDT in the Eclipse IDE) provides powerful capabilities to check the syntactic or grammar errors in AspectJ programs, it fails to detect potential semantic defects in aspect-oriented software systems. In this paper, we present XFindBugs, an eXtended FindBugs for AspectJ, to help programmers find potential bugs in AspectJ applications through static analysis. XFindBugs supports 17 bug patterns to cover common error-prone features in an aspect-oriented system, and integrates the corresponding bug detectors into the FindBugs framework. We evaluate XFindBugs on a number of large-scale open source AspectJ projects (306,800 LOC in total). In our evaluation, XFindBugs confirms 7 reported bugs and finds 257 previously unknown defects. Our experiment also indicates that the bug patterns supported in XFindBugs exist in real-world software systems, even for mature applications by experienced programmers.

DOI： 10.1145/1512475.1512490

Language：English   Publishing type：Research paper (other academic)  

Test case prioritization techniques have been empirically proved to be effective in improving the rate of fault detection in regression testing. However, most of previous techniques assume that all the faults have equal severity, which dose not meet the practice. In addition, because most of the existing techniques rely on the information gained from previous execution of test cases or source code changes, few of them can be directly applied to non-regression testing. In this paper, aiming to improve the rate of severe faults detection for both regression testing and non-regression testing, we propose a novel test case prioritization approach based on the analysis of program structure. The key idea of our approach is the evaluation of testing-importance for each module (e.g., method) covered by test cases. As a proof of concept, we implement Apros, a test case prioritization tool, and perform an empirical study on two real, non-trivial Java programs. The experimental result represents that our approach could be a promising solution to improve the rate of severe faults detection.

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ICSM.2008.4658115

Language：English   Publishing type：Research paper (other academic)  

As Aspect-Oriented Programming (AOP) wins more and more popularity, there is increasing interest in using aspects to implement crosscutting concerns in object-oriented software. During software evolution, source code editing and testing are interleaved activities to assure code quality. If regression tests fail unexpectedly after a long session of editing, it may be difficult for programmers to find out the failure causes. In this paper, we present Flota, a fault localization tool for AspectJ programs. When a regression test fails unexpectedly after a session of source changes, Flota first decomposes the differences between two program versions into a set of atomic changes, and then identifies a subset of affecting changes which is responsible for the failure. Programmers are allowed to select (and apply) suspected changes to the original program, constructing compliable intermediate versions. Thus, programmers can re-execute the failed test against these intermediate program versions to locate the exact faulty changes by iteratively selecting, applying and narrowing down the set of affecting changes. Flota is implemented on top of the ajc compiler and designed as an eclipse plugin. Our preliminary empirical study shows that Flota can assist programmers effectively to find a small set of faulty changes and provide valuable debugging support.

DOI： 10.1145/1404953.1404959

Language：English   Publishing type：Research paper (other academic)  

When regression tests fail unexpectedly after a long session of editing, it may be tedious for programmers to find out the failure-inducing changes by manually inspecting all code edits. To eliminate the expensive effort spent on debugging, we present a hybrid approach, which combines both static and dynamic analysis techniques, to automatically identify the faulty changes. Our approach first uses static change impact analysis to isolate a subset of responsible changes for a failed test, then utilizes the dynamic test execution information to rank these changes according to our proposed heuristic (indicating the likelihood that they may have contributed to the failure), and finally employs an improved Three-Phase delta debugging algorithm, working from the coarse method level to the fine statement level, to find a minimal set of faulty statements. We implemented the proposed approach for both Java and AspectJ programs in our AutoFlow prototype. In our evaluation with two third-party applications, we demonstrate that this hybrid approach can be very effective: at least for the subjective programs we investigated, it takes significantly (almost 4X) fewer tests than the original delta debugging algorithm to locate the faulty code.

DOI： 10.1145/1512475.1512492

Language：English   Publishing type：Research paper (other academic)  

Change impact analysis is a useful technique for software evolution. It determines the effects of a source editing session and provides valuable feedbacks to the programmers for making correct decisions. Recently, many techniques have been proposed to support change impact analysis of procedural or object-oriented software, but seldom effort has been made for aspect-oriented software. In this paper we propose a new change impact analysis technique for AspectJ programs. At the core of our approach is the atomic change representation which captures the semantic differences between two versions of an AspectJ program. We also present an impact analysis model, based on AspectJ call graph construction, to determine the affected program fragments, affected tests and their responsible changes. The proposed techniques have been implemented in Celadon, a change impact analysis framework for AspectJ programs. We performed an empirical evaluation on 24 versions of eight AspectJ benchmarks. The result shows that our proposed technique can effectively perform change impact analysis and provide valuable information in AspectJ software evolution.

DOI： 10.1109/ICSM.2008.4658057

Language：English   Publishing type：Research paper (other academic)  

To reduce the manual effort of assessing potential affected program parts during software evolution, we develop a tool, called Celadon, which automates the change impact analysis for AspectJ programs. Celadon is implemented in the context of the Eclipse environment and designed as a plugin. It analyzes the source code of two AspectJ software versions, and decomposes their differences into a set of atomic changes together with their dependence relationships. The analysis result is reported in terms of impacted program parts and affected tests. For each affected test, Celadon also identifies a subset of affecting changes that are responsible for the test's behavior change. In particular, as one of its applications, Celadon helps facilitate fault localization by isolating failure-inducing changes for one specific affected test from other irrelevant changes.

DOI： 10.1145/1370175.1370184

Language：English   Publishing type：Research paper (other academic)  

Points-to analysis is a fundamental analysis technique. whose results are useful in compiler optimization and software engineering tools. Although many points-to analysis algorithms have been proposed for procedural and objectoriented languages like C and Java, there is no pointsto analysis for aspect-oriented languages so far. Based on Andersen-style points-to analysis for Java, we propose flowand context-insensitive points-to analysis for AspectJ. The main idea is to perform the analysis crossing the boundary between aspects and classes. Therefore, our technique is able to handle the unique aspectual features. To investigate the effectiveness of our technique, we implement our analysis approach on top of the ajc AspectJ compiler and evaluate it on nine AspectJ benchmarks. The experimental result indicates that, compared to existing Java approaches, the proposed technique can achieve a significant higher precision and run in practical time and space.

DOI： 10.1109/SCAM.2008.30

Language：English   Publishing type：Research paper (other academic)  

In this paper, we propose a fine-grained coupling metrics suite for aspect-oriented (AO) systems, to measure software changes during system evolution. We also present a correlation model in terms of intermediate processes, for better evaluating the relation between coupling metrics and system maintainability. To investigate the practicability of our proposed model, we have implemented a coupling metrics analysis tool called A JMetries and performed an empirical study on eight AspectJ benchmarks. The experiment result suggests that our correlation model provides useful information to evaluate the maintainability of AO systems.

DOI： 10.1109/TASE.2008.17

Language：English   Publishing type：Research paper (other academic)  

Program verification is a promising approach to improving program quality. To formally verify aspectoriented programs, we have to find a way to formally specify programs written in aspect-oriented languages. Pipa is a BISL tailored to AspectJ for specifying AspectJ programs. However, Pipa has not provided specification method for pointcuts in AspectJ programs. Based on the exist work of Pipa, and related issues, this paper proposes an approach to specifying pointcuts using purity conception in JML. This paper also provides several examples to illustrate our pointcut specification approach.

DOI： 10.1109/COMPSAC.2007.196

Language：English   Publishing type：Research paper (other academic)  

A number of new modularization techniques are emerging to cope with the challenges of contemporary software engineering, such as aspect-oriented software development (AOSD), feature-oriented programming (FOP), and the like. The effective assessment of such emerging modularization technologies plays a pivotal role on: (i) a better understanding of their real benefits and drawbacks when compared to conventional development techniques, and (ii) their effective transfer to mainstream software development. The ACoM workshop is the first initiative to put together researchers and practitioners with different backgrounds in order to discuss the multi-faceted issues that emerge in the assessment and/or comparison of new modularization techniques. The workshop is strongly focused on discussions around short presentations and theme-specific groups.

DOI： 10.1109/ICSECOMPANION.2007.1

Language：English   Publishing type：Research paper (other academic)  

Bug patterns are erroneous code idioms or bad coding practices that have been proved fail time and time again. They mainly arise from the misunderstanding of language features, the use of erroneous design patterns or simple mistakes sharing the common behaviors. Aspect-oriented programming (AOP) is a new technique to separate the cross-cutting concerns for improving modularity in software design and implementation. However, there is no effective debugging technique for aspect-oriented programs until now and none of the prior researches focused on the identification of bug patterns in aspect-oriented programs. In this paper, we present six bug patterns in AspectJ programming language and show the corresponding example for each bug pattern to help to illustrate the symptoms of these patterns. We take this as the first step to provide an underlying basis on testing and debugging of AspectJ programs.

DOI： 10.1109/COMPSAC.2007.159

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented software development is gaining popularity with the adoption of aspect-oriented languages in writing programs. To reduce the manual effort in assuring the quality of aspect-oriented programs, we have developed a set of techniques and tools for automated testing of aspect-oriented programs. This position paper presents our perspectives on automated testing techniques from three dimensions: testing aspectual behavior or aspectual composition, unit tests or integration tests, and test-input generation or test oracles. We illustrate automated testing techniques primarily through the last dimension in the perspectives. By classifying these automated testing techniques in the perspectives, we provide better understanding of these techniques and identify future directions for automated testing of aspect-oriented programs. This position paper also presents a couple of new techniques that we propose based on the perspectives.

DOI： 10.1145/1229384.1229386

Language：English   Publishing type：Research paper (other academic)  

Regression testing aims at showing that code has not been adversely affected by modification activities during maintenance. Regression test selection techniques reuse tests from an existing test suite to test a modified program. By reusing such a test suite to retest modified programs, maintainers or testers can reduce the required testing effort. This paper presents a regression test selection technique for AspectJ programs. The technique is based on various types of control flow graphs that can be used to select from the original test suite test cases that execute changed code for the new version of the AspectJ program. The code-base technique operates on the control flow graphs of AspectJ programs. The technique can be applied to modified individual aspects or classes as well as the whole program that uses modified aspects or classes.

DOI： 10.1145/1146374.1146378

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented software development is gaining popularity with the adoption of languages such as AspectJ Testing is an important part in any software development, including aspect-oriented development. To automate generation of unit tests for AspectJ programs, we can apply the existing tools that automate generation of unit tests for Java programs. However, these tools can generate a large number of test inputs, and manually inspecting the behavior of the software on all these inputs is time consuming. We propose Raspect, a framework for detecting redundant unit tests for AspectJ programs. We introduce three levels of units in AspectJ programs: advised methods, advice, and intertype methods. We show how to detect at each level redundant tests that do not exercise new behavior. Our approach selects only non-redundant tests from the automatically generated test suites, thus allowing the developer to spend less time in inspecting this reduced set of tests. We have implemented Raspect and applied it on 12 subjects taken from a variety of sources; our experience shows that Raspect can effectively reduce the size of generated test suites for inspecting AspectJ programs.

DOI： 10.1109/ISSRE.2006.21

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented programming (AOP) has been proposed as a technique for improving the separation of concerns in software design and implementation. The field of AOP has, so far, focused primarily on problem analysis, language design, and implementation. Even though the importance of program comprehension and software maintenance is known, it has received little attention in the aspect-oriented paradigm. However, as the software systems coded in AOP languages are accumulated, the development of techniques and tools to support program comprehension and software maintenance tasks for aspect-oriented software will become important. In order to understand and maintain aspect-oriented programs, abstract models for representing these programs are needed. In this paper, we present techniques to construct control-flow representations for aspect-oriented programs, and discuss some applications of the representations in a program comprehension and maintenance environment.

DOI： 10.1109/QSIC.2006.20

Language：English   Publishing type：Research paper (other academic)  

Aspect-oriented software development is gaining popularity with the wider adoption of languages such as AspectJ To reduce the manual effort of testing aspects in AspectJ programs, we have developed a framework, called Aspectra, that automates generation of test inputs for testing aspectual behavior, i.e., the behavior implemented in pieces of advice or intertype methods defined in aspects. To test aspects, developers construct base classes into which the aspects are woven to form woven classes. Our approach leverages existing test-generation tools to generate test inputs for the woven classes; these test inputs indirectly exercise the aspects. To enable aspects to be exercised during test generation, Aspectra automatically synthesizes appropriate wrapper classes for woven classes. To assess the quality of the generated tests, Aspectra defines and measures aspectual branch coverage (branch coverage within aspects). To provide guidance for developers to improve test coverage, Aspectra also defines interaction coverage. We have developed tools for automating Aspectra's wrapper synthesis and coverage measurement, and applied them on testing 12 subjects taken from a variety of sources. Our experience has shown that Aspectra effectively provides tool supports in enabling existing test-generation tools to generate test inputs for improving aspectual branch coverage.

DOI： 10.1145/1119655.1119681

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ASE.2006.5

Language：English   Publishing type：Research paper (scientific journal)  

Program slicing can be effectively used to debug, test, analyze, understand and maintain object-oriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in a stepwise way, from package level, to class level, method level, and finally up to statement level. The stepwise slicing algorithm and the related graph reachability algorithms are presented, the architecture of the Java program Analyzing TOol (JATO) based on hierarchical slicing model is provided, the applications and a small case study are also discussed.

DOI： 10.1007/BF02973448

Language：English   Publishing type：Research paper (scientific journal)  

A large body of research in the measurement of software complexity at code level has been conducted, but little effort has been made to measure the architectural-level complexity of a software system. In this paper, we propose some architectural-level metrics which are appropriate for evaluating the architectural attributes of a software system. The main feature of our approach is to assess the architectural-level complexity of a software system by analyzing its formal architectural specification, and therefore the process of metric computation can be automated completely.

Language：English  

Cohesion is an internal software attribute representing the degree to which the components are bound together within a software module. Cohesion is considered to be a desirable goal in software development, leading to better values for external attributes such as maintainability, reusability, and reliability. Aspect-oriented software development (AOSD) is a new technique to support separation of concerns in software development. AOSD introduces a new kind of component called aspect which is like a class, also consisting of attributes (aspect instance variables) and those modules such as advice, introduction, pointcuts, and methods. The cohesion for such an aspect is therefore mainly about how tightly the attributes and modules of aspects cohere. To test this hypothesis, cohesion measures for aspects are needed. In this paper, we propose an approach to assessing the aspect cohesion based on dependence analysis. To this end, we present various types of dependencies between attributes and/or modules in an aspect, and the aspect dependence graph (ADG) to explicitly represent these dependencies. Based on the ADG, we formally define some aspect cohesion measures. We also discuss the properties of these dependencies, and according to these properties, we prove that these measures satisfy the properties that a good measure should have.

DOI： 10.1007/978-3-540-24721-0_4

Language：English   Publishing type：Research paper (scientific journal)  

Constructing a binary search tree of n nodes with best frequencies needs Ω(n log n) time on RAM, and Ω(log n) time on n-processor EREW, CREW, or CRCW PRAM. In this paper, we propose an O(1) time algorithm on n-processor BSR PRAM for the problem, which is the first constant time solution to the problem on any model of computation.

DOI： 10.1007/978-3-540-30501-9_48

Language：English   Publishing type：Research paper (other academic)  

A new pattern projection technique for measuring 3D topography is presented, in order to shorten the measurement time and to improve measurement accuracy. The Monochrome-Projection Color-Analysis (MPCA) technique is proposed to measure an object with a complicated surface color distribution. An optimal color channel is chosen and a single channel image for intensity calculation is compounded so that the greatest amount of information from an observation pattern image is used. Moreover, in order to measure a greater number of stripes in a single projection, Optimal Intensity-Modulation Projection (OIMP) technology is adopted. By using of combination of MPCA and OIMP, about 100 stripes are reliably detectable in a single optimal pattern projection and double image capture.

Language：English   Publishing type：Research paper (other academic)  

In Java, programs are being compiled into a portable binary format call bytecode. Every class is represented by a single class file containing class related data and bytecode instructions. Recently, more and more Java applications are routinely transmitted over the internet as compressed class file archives (i.e., zip files and jar files). However, instead of class files, the source code of applications is usually unavailable for the user, making it difficult to be understood and maintained. As a result, the development of techniques and tools to support analysis of Java bytecode programs is important. In this paper we describe a toolkit, called Kafer, that supports the development of software engineering tools for Java Bytecode programs. The Kafer is a prototype implementation of the techniques proposed in our previous work.

Language：English   Publishing type：Research paper (scientific journal)  

Pipa is a behavioral interface specification language (BISL) tailored to AspectJ, an aspect-oriented programming language. Pipa is a simple and practical extension to the Java Modeling Language (JML), a BISL for Java. Pipa uses the same basic approach as JML to specify AspectJ classes and interfaces, and extends JML, with just a few new constructs, to specify AspectJ aspects. Pipa also supports aspect specification inheritance and crosscutting. This paper discusses the goals and overall approach of Pipa. It also provides several examples of Pipa specifications and discusses how to transform an AspectJ program together with its Pipa specification into a corresponding Java program and JML specification. The goal is to facilitate the use of existing JML-based tools to verify AspectJ programs.

Language：English   Publishing type：Research paper (scientific journal)  

The current research so far in aspect-oriented software development is focused on problem analysis, software design, and implementation techniques. Even though the importance of software testing is known, it has received little attention in the aspect-oriented paradigm. In this paper, we propose a data-flow-based unit testing approach for aspect-oriented programs. Our approach tests two types of units for an aspect-oriented program, i.e., aspects that are modular units of crosscutting implementation of the program, and those classes whose behavior may be affected by one or more aspects. For each aspect or class, our approach performs three levels of testing, i.e., intra-module, inter-module, and intra-aspect or intra-class testing. For an individual module such as a piece of advice, a piece of introduction, and a method, we perform intra-module testing. For a public module along with other modules it calls in an aspect or class, we perform inter-module testing. For modules that can be accessed outside the aspect or class and can be invoked in any order by users of the aspect or class, we perform intra-aspect or intra-class testing. Our approach can handle unit testing problems that are unique to aspect-oriented programs. We use control flow graphs to compute def-use pairs of an aspect or class being tested and use such information to guide the selection of tests for the aspect or class.

Language：Japanese   Publishing type：Research paper (scientific journal)  

In a concurrent environment, due to schedule, race conditions and synchronisation among concurrent units, some program statements may never be executed. Such statements are dead statements and have no influence on the programs except making them more diff

DOI： 10.1016/S0950-5849(02)00106-4

Language：English   Publishing type：Research paper (scientific journal)  

Change impact analysis is a useful technique in software maintenance and evolution. Many techniques have been proposed to support change impact analysis at the code level of software systems, but little effort has been made for change impact analysis at the architectural level. In this paper, we present an approach to supporting change impact analysis at the architectural level of software systems based on an architectural slicing and chopping technique. The main feature of our approach is to assess the effect of changes in a software architecture by analyzing its formal architectural specification, and, therefore, the process of change impact analysis can be automated completely.

DOI： 10.1002/smr.258

Language：English   Publishing type：Research paper (scientific journal)  

Time optimal algorithms on an n-processor BSR PRAM for many n-size problems can be found in the literature. They outpace those on EREW, CREW or CRCW PRAM for the same problems. When only p (1 < p < n) processors are available, efficient algorithms on a p-processor BSR for some n-size problems can not be obtained from those on an n-processor BSR, and they have to be reconsidered. In this paper, we discuss and give two algorithms on a p-processor BSR for the two n-size problems of matching parentheses and decoding a binary tree from its bit-string, respectively, and show that they are time optimal.

Language：English   Publishing type：Research paper (other academic)  

Program slicing has many applications in software engineering activities including program comprehension, debugging, testing, maintenance, and model checking. In this paper, we propose an approach to slicing aspect-oriented software. To solve this problem, we present a dependence-based representation called aspect-oriented system dependence graph (ASDG), which extends previous dependence graphs, to represent aspect-oriented software. The ASDG of an aspect-oriented program consists of three parts: a system dependence graph for non-aspect code, a group of dependence graphs for aspect code, and some additional dependence arcs used to connect the system dependence graph to the dependence graphs for aspect code. After that, we show how to compute a static slice of an aspect-oriented program based on the ASDG.

DOI： 10.1109/WPC.2002.1021346

Language：English   Publishing type：Research paper (other academic)  

Program dependency analysis is an analysis technique to identify and determine various program dependencies in program source codes. It is an important approach for testing, understanding, maintaining and transforming programs. But, there are still many difficulties to be solved when carrying out dependency analysis for concurrent programs because the execution of statements is nondeterministic. In this paper, we propose a novel approach to analyze dependencies for concurrent Ada 95 programs. Two graphs: concurrent program flow graph and concurrent program dependency graph are developed to represent concurrent Ada programs and analyze dependency relations. The paper also presents a dependency analysis algorithm, which can obtain more precise information than most previous methods we know.

DOI： 10.1007/3-540-48046-3_17

Language：English   Publishing type：Research paper (other academic)  

A large body of research in the measurement of software complexity at code level has been studied, but little effort has been made for measuring the architectural-level complexity of a software system. In this paper, we propose some architectural metrics which are appropriate for evaluating the architectural attributes of a software system. The main feature of our approach is to assess the architectural-level complexity of a software system by analyzing its formal architectural specification, and therefore the process of metric computation can be automated completely.

Language：English   Publishing type：Research paper (scientific journal)  

Understanding control flows in a computer program is essential for many software engineering tasks such as testing, debugging, reverse engineering, and maintenance. In this paper, we present a control flow analysis technique to analyze the control flow in Java bytecode. To perform the analysis, we construct a control flow graph(CFG) for Java bytecode not only at the intraprocedural level but also at the interprocedural level. We also discuss some applications of a CFG in a maintenance environment for Java bytecode.

DOI： 10.1007/BF03160273

Language：English   Publishing type：Research paper (scientific journal)  

A program analysis system, CLPKIDS that supports development of software engineering tools for concurrent logic programs was described. The core modules of the system consisted of the program slicer, the declarative debugger and the maintenance support tool. The features of the system allowed the analysis to be performed in an unified framework that simplifies the implementation of the algorithms.

DOI： 10.1109/CMPSAC.2001.960664

Language：English   Publishing type：Research paper (scientific journal)  

Program slice has many applications such as program debugging, testing, maintenance, and complexity measurement. A static slice consists of all statements in program P that may effect the value of variable v at some point p, and a dynamic slice consists only of statements that influence the value of variable occurrence for specific program inputs. In this paper, we concern the problem of dynamic slicing of object-oriented programs which, to our knowledge, has not been addressed in the literatures. To solve this problem, we present the dynamic object-oriented dependence graph (DODG)which is an arc-classified digraph to explicitly represent various dynamic dependence between statement instances for a particular execution of an object-oriented program. Based on the DODG, we present a two-phase backward algorithm for computing a dynamic slice of an object-oriented program.

DOI： 10.1007/BF03160274

Language：English   Publishing type：Research paper (other academic)  

Program Slicing has many applications an software engineering activities. However, until recently, no slicing algorithm has been presented that can compute executable slices for concurrent logic programs. In this paper we present a dependence-graph bused approach to computing executable slice for concurrent logic programs. The dependence-bused representation used in this paper is called the Argument Dependence Net which can be wed to explicitly represent various types of program dependences an a concurrent logic program. Based on the ADN, we can compute static executable slices for concurrent logic programs at argument level.

DOI： 10.1109/APAQS.2001.989997

Language：English   Publishing type：Research paper (scientific journal)  

An alternative approach to developing reusable components from scratch is to recover them from existing systems. Although numerous techniques have been proposed to recover reusable components from existing systems, most have focused on implementation code, rather than software architecture. In this paper, we apply architectural slicing to extract reusable architectural elements (i.e., components and connectors) from the existing architectural specification of a software system. Unlike traditional program slicing, which operates on the source code of a program to provide the low-level implementation details of the program, architectural slicing directly operates on the architectural specification of a software system, and therefore can provide useful knowledge about the high-level architecture of the system.

Language：English   Publishing type：Research paper (scientific journal)  

A dependence analysis technique to Java bytecode is presented. Various types of primary dependencies in Java bytecode at the intraprocedural level are defined. Some applications of the technique in software engineering tasks related to Java bytecode development including Java bytecode slicing, understanding and testing are discussed. The technique can also be used as an underlying base to develop other software engineering tools to aid debugging, reengineering and reverse engineering for Java bytecode.

Language：English   Publishing type：Research paper (scientific journal)  

This paper introduces a new dependence analysis technique, named architectural dependence analysis to support software architecture development. In contrast to traditional dependence analysis, architectural dependence analysis is designed to operate on a formal architectural specification of a software system, rather than the source code of a conventional program. Architectural dependence analysis provides knowledge of dependences for the high-level architecture of a software system, rather than the low-level implementation details of a conventional program.

Language：English   Publishing type：Research paper (other academic)  

Understanding program dependencies in a computer program is essential for many software engineering activities including program slicing, testing, debugging, reverse engineering, and maintenance. We present a dependence-based representation called multithreaded dependence graph, which extends previous dependence-based representations, to represent program dependencies in a concurrent Java program. We also discuss some important applications of a multithreaded dependence graph in a maintenance environment for concurrent Java programs.

DOI： 10.1109/PDSE.1999.779735

Language：English   Publishing type：Research paper (other academic)  

Although many slicing algorithms have been proposed for object oriented programs, no slicing algorithm has been proposed which can be used to handle the problem of slicing concurrent Java programs correctly. We propose a slicing algorithm for concurrent Java programs. To slice concurrent Java programs, we present a dependence based representation called multithreaded dependence graph, which extends previous dependence graphs to represent concurrent Java programs. We also show how static slices of a concurrent Java program can be computed efficiently based on its multithreaded dependence graph.

DOI： 10.1109/WPC.1999.777751

Language：English   Publishing type：Research paper (other academic)  

Software maintenance is a costly process because each modification to a program must take into account many complex dependence relationships in the existing software. An understanding of program dependences is therefore an inevitable step to efficient software change. We propose a dependence based representation named the system dependence net (SDN), which extends previous dependence based representations to represent various program dependences in concurrent object oriented programs. An SDN of a concurrent object oriented program consists of a collection of dependence graphs each representing a main procedure, a free standing procedure, or a method in a class of the program. It also consists of some additional arcs to represent direct dependences between a call and the called procedure/method and transitive interprocedural data dependences. An SDN can be used to represent either object oriented features or concurrency issues in a concurrent object oriented program, and can be used as an underlying representation in a maintenance environment for concurrent object oriented programs.

DOI： 10.1109/CSMR.1998.665734

Language：English   Publishing type：Research paper (other academic)  

Software architecture is receiving increasing attention as a critical level for software systems. As software architecture design resources (in the form of architectural specifications) are going to be accumulated, the development of techniques and tools to support architectural understanding, testing, reengineering, maintenance, and reuse will become an important issue. This paper introduces a new form of slicing, named architectural slicing, to aid architectural understanding and reuse. In contrast to traditional slicing, architectural slicing is designed to operate on the architectural specification of a software system, rather than the source code of a program. Architectural slicing provides knowledge about the high-level structure of a software system, rather than the low-level implementation details of a program. In order to compute an architectural slice, we present the architecture information flow graph which can be used to represent information flows in a software architecture. Based on the graph, we give a two-phase algorithm to compute an architectural slice.

DOI： 10.1109/ICECCS.1998.706659

Language：English   Publishing type：Research paper (other academic)  

A large body of research in the measurement of software complexity has focused on imperative programs, but little effort has been made for logic programs. In this paper, complexity metrics for concurrent logic programs are proposed, which are specifically designed to quantify the information flow of concurrent logic programs. These metrics are defined based on the argument dependence net (ADN) of a concurrent logic program which is an arc-classified digraph to explicitly represent various program dependences between arguments in the program. The proposed metrics can be used to measure the complexity of a concurrent logic program from various different viewpoints.

DOI： 10.1109/CSMR.1998.665796

Language：English   Publishing type：Research paper (other academic)  

In this paper a formal model for program dependence analysis of concurrent logic programs is proposed with the following contributions. First, two language-independent program representations are presented for explicitly representing control flows and/or data flows in a concurrent logic program. Then based on these representations, program dependences between literals in concurrent logic programs are defined formally, and a dependence-based program representation named the Literal Dependence Net (LDN) is presented for explicitly representing primary program dependences in a concurrent logic program. Finally, as applications of the LDNs, some important software engineering activities including program slicing, debugging, testing, complexity measurement, and maintenance are discussed in a programming environment for concurrent logic programs.

Language：English   Publishing type：Research paper (scientific journal)  

Program slicing has many applications such as program debugging, testing, maintenance, and complexity measurement. This paper concerns the problem of slicing concurrent object-oriented programs that has not been addressed in the literature until now. To solve this problem, we propose a new program dependence representation named the system dependence net (SDN), which extends previous program dependence representations to represent concurrent object-oriented programs. An SDN of a concurrent object-oriented program consists of a collection of procedure dependence nets each representing a main procedure, a free standing procedure, or a method in a class of the program, and some additional arcs to represent direct dependences between a call and the called procedure/method and transitive interprocedural data dependences. We construct the SDN to represent not only object-oriented features but also concurrency issues in a concurrent object-oriented program. Once a concurrent object-oriented program is represented by its SDN, the slices of the program can be computed based on the SDN as a simple vertex reachability problem in the net.

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Number of peer-reviewed articles in foreign language journals：3

Proceedings of International Conference Number of peer-reviewed papers：49

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