Bridging User Feedback and System Diagnosis: Reproducing Mobile Performance Issues from Reviews

Mobile application performance is a vital factor for user experience. Yet, performance issues are notoriously di!cult to detect in development environments, where they often manifest less conspicuously, making their diagnosis more challenging. In this setting, app reviews from end users across diverse devices and usage contexts 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 "rst 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, enriching the original review with performance issue details. An execution agent is then employed to generate and execute commands to reproduce the issue. After executing all necessary steps, the system incorporates multifaceted detection methods to identify performance issues by monitoring Android logs, GUI changes, and system resource utilization during the reproduction process. Experimental results demonstrate that our proposed framework achieves a 72.73 % success rate in reproducing performance issues on the constructed dataset, outperforming the best baseline by 27.28 percentage points.

• Software and its engineering → Software testing and debugging; Software performance.