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ESEC/FSE 2022
Mon 14 - Fri 18 November 2022 Singapore
Mon 14 Nov 2022 15:00 - 15:07 at SRC LT 51 - Community Chair(s): Dirk Riehle

In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has found that bug reports with good quality tend to gain more attention than the ones with poor quality. As an essential component of an issue, title quality is an important aspect of issue quality. Moreover, issues are usually presented in a list view, where only the issue title and some metadata are present. In this case, a concise and accurate title is crucial for readers to grasp the general concept of the issue and facilitate the issue triaging. Previous work formulated the issue title generation task as a one-sentence summarization task. A sequence-to-sequence model was employed to solve this task. However, it requires a large amount of domain-specific training data to attain good performance in issue title generation. Recently, pre-trained models, which learned knowledge from large-scale general corpora, have shown much success in software engineering tasks.

In this work, we make the first attempt to fine-tune BART, which has been pre-trained using English corpora, to generate issue titles. We implemented the fine-tuned BART as a web tool named \textsc{iTiger}, which can suggest an issue title based on the issue description. \textsc{iTiger} is fine-tuned on 267,094 GitHub issues. We compared \textsc{iTiger} with the state-of-the-art method, i.e., iTAPE, on 33,438 issues. The automatic evaluation shows that \textsc{iTiger} outperforms iTAPE by 29.7%, 50.8%, and 34.1%, in terms of ROUGE-1, ROUGE-2, ROUGE-L F1-scores. The manual evaluation also demonstrates the titles generated by BART are preferred by evaluators over the titles generated by iTAPE in 72.7% of cases. Besides, the evaluators deem our tool as useful and easy-to-use. They are also interested to use our tool in the future.

\textbf{Demo URL:} https://tinyurl.com/itiger-tool

\textbf{Source code and replication package URL:} https://github.com/soarsmu/iTiger

Mon 14 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

14:00 - 15:30
CommunityResearch Papers / Ideas, Visions and Reflections / Demonstrations / Industry Paper at SRC LT 51
Chair(s): Dirk Riehle University of Bavaria, Erlangen
14:00
15m
Talk
In War and Peace: The Impact of World Politics on Software Ecosystems
Ideas, Visions and Reflections
Raula Gaikovina Kula Nara Institute of Science and Technology, Christoph Treude University of Melbourne
DOI
14:15
15m
Talk
A Retrospective Study of One Decade of Artifact Evaluations
Research Papers
Stefan Winter LMU Munich, Christopher Steven Timperley Carnegie Mellon University, Ben Hermann TU Dortmund, J├╝rgen Cito TU Wien, Jonathan Bell Northeastern University, Michael Hilton Carnegie Mellon University, Dirk Beyer LMU Munich
DOI
14:30
15m
Talk
Understanding Skills for OSS Communities on GitHub
Research Papers
Jenny T. Liang University of Washington, Thomas Zimmermann Microsoft Research, Denae Ford Microsoft Research
DOI Pre-print Media Attached
14:45
15m
Talk
Achievement Unlocked: A Case Study on Gamifying DevOps Practices in Industry
Industry Paper
Patrick Ayoup Concordia University, Diego Costa Concordia University, Canada, Emad Shihab Concordia University
DOI
15:00
7m
Talk
iTiger: An Automatic Issue Title Generation Tool
Demonstrations
Ting Zhang Singapore Management University, Ivana Clairine Irsan Singapore Management University, Ferdian Thung Singapore Management University, DongGyun Han Royal Holloway, University of London, David Lo Singapore Management University, Lingxiao Jiang Singapore Management University
15:08
7m
Talk
CodeMatcher: A Tool for Large-Scale Code Search Based on Query Semantics Matching
Demonstrations
Chao Liu Chongqing University, Xuanlin Bao Chongqing University, Xin Xia Huawei, Meng Yan Chongqing University, David Lo Singapore Management University, Ting Zhang Singapore Management University
15:15
15m
Talk
Generating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study
Research Papers
Yu Nong Washington State University, Yuzhe Ou University of Texas at Dallas, Michael Pradel University of Stuttgart, Feng Chen University of Texas at Dallas, Haipeng Cai Washington State University
DOI Pre-print