Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network
Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively.
Tue 16 MayDisplayed time zone: Hobart change
15:45 - 17:15 | Bugs and Machine Learning / Steering Committee Meeting / ClosingResearch / Journal First / Closing at Meeting Room 106 Chair(s): Banani Roy University of Saskatchewan | ||
15:45 9mFull-paper | Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network Research Yan Lei Chongqing University, Tiantian Wen , Huan Xie , Lingfeng Fu Chongqing University, Chunyan Liu Chongqing University, Lei Xu Haier Smart Home Co., Ltd., Hongxia Sun Qingdao Haidacheng Purchasing Service Co., Ltd. Pre-print Media Attached | ||
15:54 9mFull-paper | Still Confusing for Bug-Component Triaging? Deep Feature Learning and Ensemble Setting to Rescue Research Yanqi Su Australian National University, Zheming Han , Zhipeng Gao Shanghai Institute for Advanced Study of Zhejiang University, Zhenchang Xing , Qinghua Lu CSIRO’s Data61, Xiwei (Sherry) Xu CSIRO’s Data61 | ||
16:03 9mFull-paper | Understanding Bugs in Multi-Language Deep Learning Frameworks Research Zengyang Li Central China Normal University, Sicheng Wang Central China Normal University, Wenshuo Wang , Peng Liang Wuhan University, China, Ran Mo Central China Normal University, Bing Li Wuhan University Link to publication Pre-print Media Attached | ||
16:12 9mFull-paper | FVA: Assessing Function-Level Vulnerability by Integrating Flow-Sensitive Structure and Code Statement Semantic Research Chao Ni Zhejiang University, Liyu Shen Zhejiang University, Wei Wang Zhejiang University, Xiang Chen Nantong University, Xin Yin The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Lexiao Zhang School of Software Technology, Zhejiang University | ||
16:21 9mTalk | Event-Aware Precise Dynamic Slicing for Automatic Debugging of Android Applications Journal First Hsu Myat Win University of Technology Sydney (UTS), Shin Hwei Tan Southern University of Science and Technology, Yulei Sui University of New South Wales, Sydney Link to publication | ||
16:30 15mPanel | Discussion 8 Closing | ||
16:45 30mMeeting | Steering Committee Meeting and Closing Closing Alexander Serebrenik Eindhoven University of Technology, Igor Steinmacher Northern Arizona University | ||