Automatically Matching Bug Reports With Related App Reviews
This addresses the problem for software developers in efficiently linking user-reported issues to existing bug reports, though it is incremental as it applies existing deep learning methods to a specific domain task.
The paper tackled the challenge of matching user feedback in app reviews with bug reports in issue trackers by introducing DeepMatcher, an automatic deep learning approach, which achieved an average hit ratio of 0.71 and Mean Average Precision of 0.55 in evaluations on four open-source apps.
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.