Automatically Analyzing Performance Issues in Android Apps: How Far Are We?

• URL: http://arxiv.org/abs/2407.05090v2
• Date: Sat, 2 Nov 2024 12:46:53 GMT
• Title: Automatically Analyzing Performance Issues in Android Apps: How Far Are We?
• Authors: Dianshu Liao, Shidong Pan, Siyuan Yang, Yanjie Zhao, Zhenchang Xing, Xiaoyu Sun,
• Abstract summary: We conduct a large-scale comparative study of Android performance issues in real-world applications and literature. Our results show a substantial divergence exists in the primary performance concerns of researchers, developers, and users. It is crucial for our community to intensify efforts to bridge these gaps and achieve comprehensive detection and resolution of performance issues.
• Score: 15.614257662319863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Performance issues in Android applications significantly undermine users' experience, engagement, and retention, which is a long-lasting research topic in academia. Unlike functionality issues, performance issues are more difficult to diagnose and resolve due to their complex root causes, which often emerge only under specific conditions or payloads. Although many efforts haven attempt to mitigate the impact of performance issues by developing methods to automatically identify and resolve them, it remains unclear if this objective has been fulfilled, and the existing approaches indeed targeted on the most critical performance issues encountered in real-world settings. To this end, we conducted a large-scale comparative study of Android performance issues in real-world applications and literature. Specifically, we started by investigating real-world performance issues, their underlying root causes (i.e., contributing factors), and common code patterns. We then took an additional step to empirically summarize existing approaches and datasets through a literature review, assessing how well academic research reflects the real-world challenges faced by developers and users. Our comparison results show a substantial divergence exists in the primary performance concerns of researchers, developers, and users. Among all the identified factors, 57.14% have not been examined in academic research, while a substantial 76.39% remain unaddressed by existing tools, and 66.67% lack corresponding datasets. This stark contrast underscores a substantial gap in our understanding and management of performance issues. Consequently, it is crucial for our community to intensify efforts to bridge these gaps and achieve comprehensive detection and resolution of performance issues.

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