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iTiger: An Automatic Issue Title Generation Tool
Speaker (s):
ZHANG Ting
PhD Candidate
School of Computing and Information Systems
Singapore Management University
Date:
Time:
Venue:
11 November 2022, Friday
2:00pm - 2:30pm
Meeting room 4.4, Level 4
School of Computing and Information Systems 1,
Singapore Management University,
80 Stamford Road
Singapore 178902
About the Talk
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 iTiger, which can suggest an issue title based on the issue description. iTiger is fine-tuned on 267,094 GitHub issues.
We compared iTiger with the state-of-the-art method, i.e., iTAPE, on 33,438 issues.
The automatic evaluation shows that 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.
Demo URL: https://tinyurl.com/itiger-tool
Source code and replication package URL: https://github.com/soarsmu/iTiger
This is a Pre-Conference talk for ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022).
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About the Speaker
ZHANG Ting is a Ph.D. candidate at SMU SCIS, supervised by Prof. David Lo and Prof. Lingxiao Jiang. Her research focuses on automatic software bug management, from detecting duplicate bug reports to repairing API misuse bugs. More information is available on https://happygirlzt.com/academic
