Performance plays a critical role in ensuring the smooth operation of any mobile application, directly influencing user engagement and retention. Android applications are no exception. However, unlike functionality issues, performance issues are more challenging to discover as their root causes are sophisticated and typically emerge under specific payloads. To tackle this problem, researchers have dedicated substantial efforts to proposing automatic approaches for understanding, detecting, and resolving performance issues. Despite these endeavors, it still remains unknown what the status quo of Android performance analysis is, and whether existing approaches can indeed accurately reflect real performance issues. To fill this research gap, we conducted a systematic literature review followed by an explanatory study to explore relevant studies and real-world challenges. Our findings reveal that current tools have limited capabilities, covering only 17.50% of the performance issues. Additionally, existing datasets encompass only 27.50% of the issues and are very limited in size. We also show real-world issue patterns, underscoring the huge gap between the identified techniques and practical concerns. Furthermore, possible solutions are provided to guide future research towards achieving effective performance issue detection and resolution.

翻译：性能对于确保任何移动应用程序的平稳运行起着至关重要的作用，它直接影响用户参与度和留存率。Android应用程序亦不例外。然而，与功能性问题不同，性能问题因其根源复杂且通常在特定负载下显现而更难以发现。为解决这一问题，研究者们投入了大量精力，致力于提出自动化方法来理解、检测和解决性能问题。尽管已有这些努力，Android性能分析的现状究竟如何，以及现有方法是否真能准确反映实际性能问题，仍然未知。为填补这一研究空白，我们首先进行了系统性文献综述，随后开展了一项解释性研究，以探索相关研究和现实挑战。我们的研究结果表明，当前工具的能力有限，仅覆盖了17.50%的性能问题。此外，现有数据集仅包含27.50%的问题，且规模非常有限。我们还揭示了现实世界中的问题模式，凸显了现有识别技术与实际关切之间的巨大差距。最后，我们提供了可能的解决方案，以指导未来研究朝着实现有效的性能问题检测与解决方向迈进。