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.

翻译：Android应用程序中的性能问题严重损害了用户体验、参与度和留存率，这一直是学术界长期关注的研究课题。与功能性问题不同，性能问题由于其复杂的根本原因而更难以诊断和解决，这些问题通常仅在特定条件或负载下才会显现。尽管已有许多研究致力于开发自动识别和解决性能问题的方法以减轻其影响，但目前尚不清楚这一目标是否已实现，以及现有方法是否确实针对了现实环境中遇到的最关键性能问题。为此，我们对现实应用和文献中的Android性能问题进行了大规模比较研究。具体而言，我们首先调查了现实中的性能问题、其根本原因（即促成因素）以及常见的代码模式。随后，我们通过文献综述进一步实证总结了现有方法和数据集，评估了学术研究在多大程度上反映了开发者和用户面临的现实挑战。我们的比较结果表明，研究人员、开发者和用户关注的主要性能问题存在显著差异。在所有已识别的因素中，57.14%尚未在学术研究中得到探讨，而高达76.39%的因素未被现有工具处理，66.67%缺乏相应的数据集。这种鲜明对比凸显了我们在理解和管理性能问题方面存在的巨大差距。因此，学术界亟需加大努力弥合这些差距，以实现对性能问题的全面检测与解决。