| 1. |
Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu and Jianjun Zhao, DeepStellar: Model-Based Quantitative Analysis of Stateful Deep Learning Systems, The 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019), 2019.08. |
| 2. |
Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, and Simon See, DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks, The 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019), 2019.07. |
| 3. |
Chao Xie, Hua Qi, Lei Ma, and Jianjun Zhao, DeepVisual: A Visual Programming Tool for Deep Learning Systems, The 27th IEEE/ACM International Conference on Program Comprehension (ICPC 2019), 130-134, 2019.05. |
| 4. |
Chao Xie, Hua Qi, Lei Ma, and Jianjun Zhao, API Recommendation for Event-Driven Android Application Development, Information and Software Technology, 30-47, 2019.03. |
| 5. |
Lei Ma, Felix Juefei-Xu, Minhui Xue, Bo Li, Li Li, Yang Liu, and Jianjun Zhao, DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems, The 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019), 535-539, 2019.02. |
| 6. |
Qiang Hu, Lei Ma, and Jianjun Zhao, DeepGraph: A PyCharm Tool for Visualizing and Understanding Deep Learning Models, The 25th Asia-Pacific Software Engineering Conference (APSEC 2018), 628-632, 2018.12. |
| 7. |
Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang, DeepMutation: Mutation Testing of Deep Learning Systems, 29th IEEE International Symposium on Software Reliability Engineering (ISSRE 2018), 100-111, 2018.10. |
| 8. |
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Chunyang Chen, Ting Su, Minhui Xue, Bo Li, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang, DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems, The 33th IEEE/ACM Conference on Automated Software Engineering (ASE 2018), 368-378, 2018.09, 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.. |
| 9. |
Anil Karna, Yuting Chen, Haibo Yu, Hao Zhong, Jianjun Zhao, The Role of Model Checking in Software Engineering, Frontiers of Computer Science, 12, 4, 642-668, 2018.08. |
| 10. |
Gefei Zhang and Jianjun Zhao., Visualizing Interactions in AngularJS-based Single Page Web Applications, The 30th International Conference on Software Engineering & Knowledge Engineering (SEKE 2018), 2018.07. |
| 11. |
Anil Karna, Jinbo Du, Haihao Shen, Hao Zhong, Jiong Gong, Haibo Yu, Xiangning Ma, Jianjun Zhao, Tuning Parallel Symbolic Execution Engine for Better Performance, Frontiers of Computer Science, 12, １, 88-100, 2018.02. |
| 12. |
Ziyi Lin, Yilei Zhou, Hao Zhong, Yuting Chen, Haibo Yu, Jianjun Zhao, SPDebugger: A Fine-grained Deterministic Debugger for Concurrency Code, The IEICE Transactions on Information and Systems, Vol. E100-D, No. 3, 473-482, 2017.03. |
| 13. |
Xiao Cheng, Zhiming Peng, Lingxiao Jiang, Hao Zhong, Haibo Yu, Jianjun Zhao, CLCMiner: Detecting Cross-Language Clones without Intermediates, The IEICE Transactions on Information and Systems, Vol. E100-D, No. 2, 273-284, 2017.02. |
| 14. |
Ziyi Lin, Hao Zhong, Yuting Chen, Jianjun Zhao, LockPeeker: Detecting Latent Locks in Java APIs, The 31th IEEE/ACM Conference on Automated Software Engineering (ASE 2016), 368-378, 2016.09. |
| 15. |
Xiao Cheng, Zhiming Peng, Linxiao Jiang, Hao Zhong, Haibo Yu, Jianjun Zhao, Detecting Cross-Language Clones Without Intermediates, The 31th IEEE/ACM Conference on Automated Software Engineering (ASE 2016) (Short Paper), 696-701, 2016.09. |
| 16. |
Xiao Cheng, Linxiao Jiang, Hao Zhong, Haibo Yu, Jianjun Zhao, On the Feasibility of Detecting Cross-Platform Code Clones via Identifier Similarity, The Fifth International Workshop on Software Mining (SoftwareMining 2016, co-located with ASE 2016) , 39-42, 2016.09. |
| 17. |
Yuting Chen, Ting Su, Chengnian Sun, Zhendong Su, Jianjun Zhao, Coverage-Directed Differential Testing of JVM Implementations, The ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2016), 85-99, 2016.06. |
| 18. |
Xiao Cheng, Hao Zhong, Yuting Chen, Zhenjiang Hu, Jianjun Zhao, Rule-Directed Code Clone Synchronization, The 24th International Conference on Program Comprehension (ICPC 2016), 2016.05. |
| 19. |
Lei Ma, Bing Yu, Cheng Zhang, Jianjun Zhao, Retrofitting Automatic Testing through Library Tests Reusing, The 24th International Conference on Program Comprehension (ICPC 2016), (Short paper), 2016.05. |
| 20. |
Xiao Cheng, Yuting Chen, Zhenjiang Hu, Tao Zan, Mengyu Liu, Hao Zhong, Jianjun Zhao, Supporting Selective Undo for Refactoring, The 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016), 13-23, 2016.03. |
| 21. |
Jiabin Ye, Cheng Zhang, Lei Ma, Haibo Yu, Jianjun Zhao, Efficient and Precise Dynamic Slicing for Client-Side JavaScript Programs, The 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016) (Best Paper Candidate Award), 449-459, 2016.03, JavaScript is the de facto dominant programming language for developing web applications. Most popular websites are using JavaScript, especially to develop client-side features. Being syntactically flexible and highly dynamic, JavaScript is easy to use and productive, but its code is known to be less maintainable. The task of maintaining client-side JavaScript code is further complicated by the pervasive interactions between JavaScript code and HTML elements, through browsers. In this paper, we present JS-Slicer, a dynamic slicer for JavaScript, to ease the task of understanding and debugging practical client-side JavaScript code. JS-Slicer defines three types of dependences, including data dependences, control dependences, and DOM dependences, to capture all relationships between program elements. JS-Slicer extends a novel dynamic analysis framework and combines dynamic and static analysis to precisely capture the dependences at run-time. A lot of language specific issues are properly handled, which enables JS-Slicer to slice practical JavaScript code. Our evaluation on six real-world web applications and JavaScript libraries shows that JS-Slicer is both precise and efficient: on average it captures around 40K dependences in 2.5K lines of code, in less than 3.0 seconds.. |
| 22. |
Ziyi Lin, Darko Maninov, Hao Zhong, Yuting Chen, Jianjun Zhao, JaConTeBe: A Benchmark Suite of Real-World Java Concurrency Bugs, The 30th IEEE/ACM Conference on Automated Software Engineering (ASE 2015), 71-80, 2015.11. |
| 23. |
Fei Lv, Hongyu Zhang, Jianguang Lou, Shaowei Wang, Dongmei Zhang, Jianjun Zhao, CodeHow: Effective Code Search Based on API Understanding and Extended Boolean, The 30th IEEE/ACM Conference on Automated Software Engineering (ASE 2015), 260-270, 2015.11. |
| 24. |
Christoph Bockisch, Marnix van ’t Riet, Haihan Yin, Mehmet Aksit, Ziyi Lin, Yuting Chen, Jianjun Zhao, Trace-based Debugging for Advanced-Dispatching Programming Languages, The 10th Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems Workshop (ICOOOLPS 2015), co-located with ECOOP 2015, 2015.07. |
| 25. |
Qi Wang, Jingyu Zhou, Yuting Chen, Yizhou Zhang, Jianjun Zhao, Extracting URLs from JavaScript via program analysis, The 9th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2013), New Ideas Track, 2013.08. |
| 26. |
Qiang Sun, Yuting Chen, Jianjun Zhao, Constraint-Based Locality Analysis for X10 Programs, The ACM SIGPLAN 2013 Workshop on Partial Evaluation and Program Manipulation (PEPM 2013), 137-146, 2013.01. |
| 27. |
Cheng Zhang, Juyuan Yang, Yi Zhang, Jing Fan, Xin Zhang, Jianjun Zhao, Peizhao Ou, Automatic Parameter Recommendation for Practical API Usage, The 34th International Conference on Software Engineering (ICSE 2012), 826-836, 2012.06. |
| 28. |
Cheng Zhang, Hao Xu, Sai Zhang, Jianjun Zhao, Yuting Chen, Frequency Estimation of Virtual Call Targets for Object-Oriented Programs, Proc. 25th European Conference on Object-Oriented Programming (ECOOP 2011), 510-532, 2011.07. |
| 29. |
Qiang Sun, Jianjun Zhao, Yuting Chen, Probabilistic Points-to Analysis for Java, The 2011 International Conference on Compiler Construction (CC 2011), 62-81, 2011.03. |
| 30. |
Cheng Zhang, Dacong Yan, Shengqian Yang, Jianjun Zhao, Yuting Chen, BPGen: An Automated Breakpoint Generator for Debugging, 32th International Conference on Software Engineering (ICSE 2010), Formal Demonstration Track, 171-174, Vol.2, 2010.05. |
| 31. |
Qingzhou Luo, Sai Zhang, Jianjun Zhao, Min Hu, A Lightweight and Portable Approach to Making Concurrent Failures Reproducible, The Fundamental Approaches to Software Engineering (FASE 2010), 323-337, 2010.03. |
| 32. |
Yu Lin, Xucheng Tang, Yuting Chen, Jianjun Zhao, A Divergence-Orietned Approach to Adaptive Random Testing of Java Programs, The 24th IEEE/ACM International Conference on Automated Software Engineering (ASE 2009), 221-232, 2009.11. |
| 33. |
Martin Gorg, Jianjun Zhao, Identifying Semantic Differences in AspectJ Programs, The ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA 2009), 25-36, 2009.07. |
| 34. |
Sai Zhang, Yu Lin, Zhongxian Gu, Jianjun Zhao, Effective Identification of Failure-Inducing Changes: A Hybrid Approach, The 8th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering (PASTE 2008), 77-83, 2008.11. |
| 35. |
Haibo Shen, Sai Zhang, Jianjun Zhao, Jianhong Fang, Shiyuan Rao, XFindBugs: eXtended FindBugs for AspectJ, The 8th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering (PASTE 2008), 70-76, 2008.11. |
| 36. |
Tao Xie, Jianjun Zhao, A Framework and Tool Supports for Generating Test Inputs of AspectJ Programs, The 5th International Conference on Aspect-Oriented Software Development (AOSD 2006), 190-201, 2006.03. |
| 37. |
Jianjun Zhao, Hongji Yang, Limin Xiang, Baowen Xu, Change Impact Analysis to Support Architectural Evolution, Journal of Software Maintenance and Evolution: Research and Practice, Vol.14, No.5, 317-333, 2002.01. |
