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CMind: An AI Agent for Localizing C Memory Bugs

Chia-Yi Su, Collin McMillan

TL;DR

CMind presents a tool-augmented AI agent for localizing memory bugs in C, marrying LLM reasoning with guided decision making to mimic human programmer behavior. It structures the localization process into entry-point identification, targeted static analysis, and hypothesis-driven reasoning, implemented as a CLI and web interface with a leash that constrains LLM outputs. Evaluated on 20 real memory bugs, CMind achieves up to 80% accuracy with GPT-5 mini and reveals limitations when stack traces are unclear, motivating robust prompting and integration with human-guided workflows. The work contributes an empirically informed, agent-based framework for bug localization and provides open-source access to both code and results for broader adoption and further study.

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.

CMind: An AI Agent for Localizing C Memory Bugs

TL;DR

CMind presents a tool-augmented AI agent for localizing memory bugs in C, marrying LLM reasoning with guided decision making to mimic human programmer behavior. It structures the localization process into entry-point identification, targeted static analysis, and hypothesis-driven reasoning, implemented as a CLI and web interface with a leash that constrains LLM outputs. Evaluated on 20 real memory bugs, CMind achieves up to 80% accuracy with GPT-5 mini and reveals limitations when stack traces are unclear, motivating robust prompting and integration with human-guided workflows. The work contributes an empirically informed, agent-based framework for bug localization and provides open-source access to both code and results for broader adoption and further study.

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.
Paper Structure (14 sections, 2 figures, 1 table)

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background/Related Work
  3. Approach
  4. Information Collection
  5. Static Analysis
  6. Bug Reasoner
  7. Language Models
  8. Web-based Interface
  9. Evaluation
  10. Dataset
  11. Preliminary Results
  12. Example
  13. Conclusion
  14. Future Works

Figures (2)

  • Figure 1: Overview of CMind. Area 1 is collects the entry point and related functions. Area 2 generates the static analysis information. Area 3 reasons the bugs based on all given information. Area 4 requests more information. Area 5 returns the requested information to the bug reasoner.
  • Figure 2: Web-based Interface for CMind