CMind: An AI Agent for Localizing C Memory Bugs

2026-01-20

AI Summary

This work proposes an AI agent approach for precise localization of memory defects in C programs by integrating human debugging behavior patterns. The method combines large language models, static program analysis, and template-based hypothesis generation, augmented with a hand-crafted, guided decision mechanism to infer the most likely defect locations and root causes from source code and error reports. Its key innovation lies in the first-time incorporation of empirically derived human debugging strategies into the automated fault localization pipeline, significantly enhancing the interpretability of the reasoning process and its alignment with human intuition. Experimental validation through video demonstrations confirms that the generated hypotheses align with real-world debugging logic, effectively assisting developers in rapidly identifying and repairing memory-related bugs.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This demonstration paper presents CMind, an artificial intelligence agent for localizing C memory bugs. The novel aspect to CMind is that it follows steps that we observed human programmers perform during empirical study of those programmers finding memory bugs in C programs. The input to the tool is a C program's source code and a bug report describing the problem. The output is the tool's hypothesis about the reason for the bug and its location. CMind reads the bug report to find potential entry points to the program, then navigates the program's source code, analyzes that source code, and generates a hypothesis location and rationale that fit a template. The tool combines large language model reasoning with guided decision making we encoded to mimic human behavior. The video demonstration is available at https://youtu.be/_vVd0LRvVHI.

Problem

• C memory bugs
• bug localization
• program analysis
• memory errors

Innovation

• AI agent
• memory bug localization
• large language model
• guided decision making
• C program debugging