Source-Free Detection and Impact Analysis of Compiler Optimization Problems in Mobile Applications
2026-06-22 • Software Engineering
Software Engineering
AI summaryⓘ
The authors found that many mobile apps run parts of their code with low compiler optimization, which slows down performance, but this issue is hard to spot since it doesn't cause errors. They created a tool called OptDetect that can find these problems from app files without needing the app's source code. Testing thousands of apps, they saw that nearly one-third of native libraries use poor optimization, affecting most apps. Fixing these issues in tested apps improved performance and user ratings. They also discovered that many third-party libraries are distributed with low optimization, suggesting the problem is widespread and needs better industry attention.
mobile appscompiler optimizationnative librariesbinary analysisperformance issuesdisassemblythird-party librariespower consumptionCPU instructionssoftware optimization
Authors
Han Hu, Xiaoheng Xie, Bo Sun, Jian Gu, Gang Fan, Li Li
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.