Still Confusing for Bug-Component Triaging? Deep Feature Learning and Ensemble Setting to Rescue
To speed up the bug-fixing process, it is essential to triage bugs into the right components as soon as possible. Given the large number of bugs filed everyday, a reliable and effective bug-component triaging tool is needed to assist this task. LR- BKG is the state-of-the-art toolkit for doing this. However, the suboptimal performance for recommending the right component at the first position (low Top-1 accuracy) limits its usage in practice. We thoroughly investigate the limitations of LR-BKG and find out the gap between the manual feature design of LR-BKG and the characteristics of bug reports causes such suboptimal performance. Therefore, we propose an approach, DEEPTRIAG, which uses the large scale pre-trained models to extract deep features automatically from bug reports (including bug summary and description), to fill this gap. DEEPTRIAG transforms bug-component triaging into a multi-classification task (CodeBERT-Classifier) and a generation task (CodeT5- Generator). Then, we ensemble the prediction results from them to improve the performance of bug-component triaging further. Extensive experimental results demonstrate the superior performance of DEEPTRIAG on bug-component triaging over LR- BKG. In particular, the overall Top-1 accuracy is improved from 56.2% to 68.3% on Mozilla dataset and from 51.3% to 64.1% on Eclipse dataset, which verifies the effectiveness and generalization of our approach on improving the practical usage for bug-component triaging.
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 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 | ||