An End-to-End Approach for Fixing Concurrency Bugs via SHB-Based Context Extractor

2026-04-07

AI Summary

Concurrent programs are prone to elusive bugs due to the nondeterministic nature of thread execution, and existing automated repair techniques often rely on idealized contextual assumptions, limiting their effectiveness in real-world scenarios. This work proposes ConFixAgent, the first end-to-end large language model (LLM)-driven agent capable of repairing concurrency bugs without requiring prior defect information. ConFixAgent automatically extracts critical contextual dependencies via static Happens-Before graphs to accurately identify concurrency defects and generate precise repairs. Evaluated across multiple benchmark datasets, ConFixAgent significantly outperforms state-of-the-art tools, effectively fixing a wide range of concurrency bugs. Its context extraction mechanism substantially enhances the accuracy of LLM-based program repair by providing semantically relevant and execution-aware information.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rise of multi-core processors and distributed systems, concurrent programming has become essential yet challenging, primarily due to the non-deterministic nature of thread execution. Manually addressing concurrency bugs is time-consuming and error-prone. Automated Program Repair techniques provide a promising solution. However, developing an end-to-end concurrency bug repair tool is particularly challenging. Most existing tools rely on the assumption that bug-related information is readily available or that concurrency bug contexts are ideally extracted, which is often impractical in real-world scenarios. This paper introduces ConFixAgent, an LLM-driven agent capable of fixing various types of concurrency bugs in an end-to-end manner, eliminating the need for any prior bug-related information. Specifically, we propose a novel context extraction approach designed for concurrency bug repair, utilizing Static Happens-Before Graphs to identify bug-relevant sections.We implemented ConFixAgent and evaluated it across multiple benchmark sets. Our extensive experiments demonstrate that ConFixAgent significantly outperforms state-of-the-art tools in addressing diverse types of concurrency bugs, with its context extraction method markedly enhancing the accuracy of LLM-generated repair solutions.

Problem

• concurrency bugs
• automated program repair
• context extraction
• non-deterministic execution
• multi-core systems

Innovation

• concurrency bug repair
• Static Happens-Before Graph
• LLM-driven agent
• context extraction
• end-to-end program repair