Automatically Matching Bug Reports With Related App Reviews
Automatically Matching Bug Reports With Related App Reviews
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DOI：
10.1109/icse43902.2021.00092
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Marlo Häring;Christoph Stanik;W. Maalej
中科院分区：
文献类型：
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作者：
Marlo Häring;Christoph Stanik;W. Maalej
应用商店允许用户对应用程序提供有价值的反馈，开发人员可以找到这些反馈并将其用于软件发展。然而，在问题跟踪器中找到与现有错误报告相匹配的用户反馈是一项挑战，因为用户和开发人员通常使用不同的语言。在这项工作中，我们介绍了DeepMatcher，这是一种自动方法，使用最先进的深度学习方法将应用程序评论中的问题报告与问题跟踪器中的错误报告相匹配。我们用四个开源应用程序对DeepMatcher进行了定量和定性评估。DeepMatcher的平均命中率为0.71，平均精度为0.55。对于91个问题报告，DeepMatcher没有找到任何匹配的bug报告。当手动分析这91个问题报告和所研究应用的问题跟踪器时，我们发现在47个案例中，用户实际上在开发人员发现并记录在问题跟踪器中之前就描述了问题。我们讨论了我们的发现和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.