MOA: A Profiling-Guided LLM Framework for Memory-Optimization Automation at Codebase Scale

2025-01-29 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

MOA is an LLM-driven framework designed to automate the detection and repair of recurring memory inefficiencies across large-scale software codebases. Operating through three specialized agents—an Analyzer, a Checker Generator, and a Patcher—MOA addresses challenges in localizing inefficiencies, scaling detection, and generating stable fixes. The Analyzer mines anti-patterns from profiling data, the Checker Generator synthesizes static analyzers, and the Patcher generates optimization patches using state-machine-driven workflows. Evaluated on OpenHarmony, an open-source operating system with over 100 million lines of C/C++ code, MOA identified 13 anti-patterns (9 previously unknown) from 3 profiled services. It detected over 10,000 inefficiencies across 7 services and generated 769 patches with a 92.5% expert acceptance rate, achieving an average 42.2% heap reduction and 10.6% binary size reduction.

Key takeaway

For performance engineers or software architects managing large C/C++ codebases, MOA offers a robust solution to persistent memory inefficiencies. You should consider integrating such an LLM-driven framework to automate the discovery of novel anti-patterns and scale their remediation. This approach significantly reduces manual effort and achieves substantial memory and binary size reductions, making it a practical tool for maintaining system health and optimizing resource costs.

Key insights

MOA automates codebase-scale memory optimization by integrating profiling, LLM-driven pattern mining, static checker synthesis, and patch generation.

Principles

• Combine dynamic profiling with static analysis.
• LLMs abstract runtime symptoms into patterns.
• Context-aware patching handles cross-file dependencies.

Method

MOA's three-stage workflow involves an Analyzer mining anti-patterns from profiling data, a Checker Generator synthesizing static analyzers, and a Patcher generating validated optimization patches via a state machine.

In practice

• Identify novel memory anti-patterns from profiling.
• Synthesize static checkers for codebase-wide detection.
• Automate multi-location, cross-file code refactoring.

Topics

• LLM-driven Automation
• Memory Optimization
• Static Analysis
• Dynamic Profiling
• OpenHarmony
• C/C++ Codebases

Code references

• gperftools/gperftools
• facebook/infer
• semgrep/semgrep
• virustotal/yara

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.