  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.
