Automatically Matching Bug Reports With Related App Reviews

Abstract

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 stateof-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.

RoseMatcher: Identifying the impact of user reviews on app updates

“…Wang et al [47] tried to match user reviews with app release notes in Spotify and manually labeled types of the user reviews. Häring et al [48] proposed an automatic approach DeepMatcher to extract problem reports from app reviews submitted by users, and then identify matching bug reports in an issue tracker used by the development team, which can help developers identify bugs earlier, enhance bug reports with user feedback, and eventually lead to more precise ways to detect duplicate or similar bugs. Palomba et al proposed an approach named CRISTAL [49] to trace informative crowd reviews into code changes, in order to monitor the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings.…”