一句话：提出 iTiger 工具，利用预训练模型自动生成 issue 标题，缓解标题生成对领域标注数据的依赖。
论文关注 issue/bug report 的标题质量问题：标题是 issue 的核心组成部分，在列表视图下，简洁准确的标题有助于读者快速理解 issue 并促进分诊。已有工作将 issue 标题生成视为单句摘要任务，采用序列到序列模型，但需要大量领域特定训练数据才能取得良好性能。为此，作者提出自动 issue 标题生成工具 iTiger，借助从大规模通用语料中学习知识的预训练模型来缓解领域数据依赖，提升标题生成效果。摘要未报告具体评估指标或结果。
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.
@inproceedings{paperbot7024,
title = {iTiger: an automatic issue title generation tool},
author = {Ting Zhang and Ivana Clairine Irsan and Ferdian Thung and DongGyun Han and David Lo and Lingxiao Jiang},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558934}
}