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

2024-07-06

arXiv.org

AI Summary

Performance issues in Android applications remain persistent due to their elusive root causes and complex triggering conditions. To address this, we conduct a large-scale empirical study of 576 real-world apps, systematically characterizing performance problems and proposing a five-dimensional analytical framework—comprising *problem*, *root cause*, *code pattern*, *tooling support*, and *available data*. Integrating systematic literature review (SLR) with industrial tool evaluation, we quantitatively reveal critical gaps: 57.14% of empirically observed root causes are unaddressed in academic literature; 76.39% lack effective automated detection tools; and 66.67% have no publicly available benchmark datasets. Furthermore, we identify a substantial misalignment between academic research priorities and industrial practice, exposing key blind spots in current work. Our findings provide targeted guidance for future performance analysis research and advocate a new paradigm—one that comprehensively covers all root causes, ensures reproducibility, and enables rigorous validation.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Solvers and Tools
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Identifying Android performance issues in real applications versus literature
• Assessing gaps between academic research and real-world developer challenges
• Evaluating coverage of performance factors by existing tools and datasets

Innovation

• Comparative study of real-world Android performance issues
• Identified gaps between academic research and developer needs
• Empirical analysis of performance issue root causes