一句话：提出 DeepMatcher，利用深度学习将应用评论中的问题报告自动匹配到缺陷跟踪系统中的 bug 报告。
应用商店评论蕴含用户对应用的有价值反馈，但用户与开发者常使用不同语言，导致评论中的问题报告难以与缺陷跟踪系统中的 bug 报告对应。本文提出 DeepMatcher，一种基于最先进深度学习方法的自动匹配技术，用于将应用评论中的问题报告关联到 issue tracker 中的 bug 报告。作者在四个开源应用上进行了定量与定性评估：DeepMatcher 平均达到 0.71 的命中率和 0.55 的平均精度均值。对于 91 个未找到任何匹配 bug 报告的问题报告，人工分析这些报告与所研究应用的 issue tracker 后发现，其中 47 例用户实际上在开发者发现并记录问题之前就已经描述了该问题。文章最后讨论了这些发现以及 DeepMatcher 的不同用例。
App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report. When manually analyzing these 91 problem reports and the issue trackers of the studied apps, we found that in 47 cases, users actually described a problem before developers discovered and documented it in the issue tracker. We discuss our findings and different use cases for DeepMatcher.