Source-Free Detection and Impact Analysis of Compiler Optimization Problems in Mobile Applications

2026-06-22

AI Summary

This work addresses the pervasive yet elusive performance issues in mobile applications—such as UI jank and thermal throttling—caused by native libraries compiled with low-level optimization flags (e.g., O0/O1), which are notoriously difficult to detect. To tackle this, we propose OptDetect, the first end-to-end framework capable of identifying mixed optimization levels without requiring source code or build metadata. OptDetect leverages binary disassembly, machine learning–driven block-level optimization classification, and a weighted scoring aggregation scheme to accurately pinpoint under-optimized code segments. Evaluation on 830 popular apps reveals that 91.7% are affected by poorly optimized third-party libraries. Post-remediation results demonstrate up to a 63% reduction in CPU instructions, a median 42% drop in user-reported performance complaints, and a 0.14-point increase in app store ratings, confirming both the efficacy and industry-wide applicability of our approach.

Abstract

Mobile apps frequently suffer from performance issues such as frame drops, overheating, and excessive power consumption. While developers optimize algorithms and debug code, a critical bottleneck often goes unnoticed: native libraries compiled with low optimization levels (O0/O1 instead of O2/O3). Because these libraries execute without functional errors, the resulting performance degradation remains hidden in production apps, affecting millions of users. We present \textsc{OptDetect}, a source-free framework that detects compiler optimization problems directly from app binaries without requiring source code or build metadata. \textsc{OptDetect} handles mixed optimization levels within a single binary through a pipeline of binary disassembly, chunk-level classification, and weighted score aggregation, achieving 93.0\% accuracy on controlled datasets and 81.9\% on real-world datasets. Applying \textsc{OptDetect} to 21,972 native libraries from 830 top-ranked Google Play apps, we find that 30.5\% of libraries use low optimization levels, affecting 91.7\% of apps. Through case studies on 12 production apps (6 commercial, 6 open-source), we demonstrate that fixing detected issues reduces CPU instructions by 10-63\% (median: 20.5\%) for commercial apps and 15-58\% (median: 32\%) for open-source apps, with performance complaints decreasing by a median of 42\% and ratings increasing by a median of 0.14 points. Further investigation reveals a previously overlooked root cause: widely-used third-party libraries are themselves distributed at low optimization levels, with 49.7\% of 1,073 libraries in a major repository exhibiting this problem. These findings highlight the need for automated detection tools and industry-wide optimization standards.

Problem

• compiler optimization
• mobile applications
• performance degradation
• native libraries
• source-free detection

Innovation

• source-free detection
• compiler optimization
• binary analysis
• mobile performance
• third-party libraries