OptiMine: Scalable and Precise Code Optimization for Android Apps via LLM-Driven Semantic Analysis

Abstract

As Android applications grow in scale, latent performance issues increasingly degrade user experience and business outcomes, yet systematically identifying optimization opportunities in large codebases remains challenging. We present OptiMine, a hybrid knowledge-to-code framework that integrates large language models (LLMs) with static program analysis to automatically uncover actionable performance optimizations. OptiMine systematically transforms unstructured expert knowledge from documents, commit diffs, and reports into structured Optimization Signatures, enabling reproducible and context-aware program reasoning. These signatures drive scalable candidate retrieval via declarative Datalog queries, while LLM-guided semantic validation performs precise applicability checking, side-effect analysis, and impact-aware ranking. We evaluate OptiMine on a public benchmark and a large industrial Android codebase. The results show that OptiMine achieves higher precision and broader coverage than heuristic- and pattern-based baselines, while scaling effectively to industrial settings. Overall, OptiMine enables reliable and scalable performance auditing, bridging expert knowledge and actionable performance improvements in real-world mobile systems.