From Feedback to Failure: Automated Android Performance Issue Reproduction
Zhengquan Li, Zhenhao Li, Zishuo Ding

TL;DR
RevPerf is an automated system that leverages user reviews and advanced techniques to reproduce Android performance issues, aiding developers in diagnosing problems more effectively.
Contribution
This paper introduces RevPerf, the first approach to automatically reproduce mobile performance issues by synthesizing app reviews and executing reproduction commands.
Findings
Achieves 72.73% success rate in reproducing issues
Outperforms baseline by 27.28%
Effectively leverages app reviews for issue diagnosis
Abstract
Mobile application performance is a vital factor for user experience. Yet, performance issues are notoriously difficult to detect in development environments, where they often manifest less conspicuously, making their diagnosis more challenging. In this setting, app reviews from users with diverse device configurations can provide timely and context-rich information about emerging performance issues. However, unlike structured bug reports, app reviews are written by end-users and tend to be more ambiguous, with individual reviews often providing only partial descriptions of the underlying issue. To bridge this gap, we present RevPerf, the first approach to automatically reproduce mobile application performance issues by leveraging and synthesizing information from app reviews. RevPerf retrieves complementary reviews via semantic retrieval and uses prompt engineering to integrate them,…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Testing and Debugging Techniques · Green IT and Sustainability
