# Automated Repair of C Programs Using Large Language Models

arXiv: https://arxiv.org/abs/2509.01947 · HTML: https://mlsanity.com/paper/2509.01947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2025.

## Built on by (most cited first, 2 of 2)

- [Reducing Cost of LLM Agents with Trajectory Reduction](https://mlsanity.com/paper/2509.23586/) (2025)
- [PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents](https://mlsanity.com/paper/2605.08468/) (2026)

## Abstract (arXiv)

This study explores the potential of Large Language Models (LLMs) in automating the repair of C programs. We present a framework that integrates spectrum-based fault localization (SBFL), runtime feedback, and Chain-of-Thought-structured prompting into an autonomous repair loop. Unlike prior approaches, our method explicitly combines statistical program analysis with LLM reasoning. The iterative repair cycle leverages a structured Chain-of-Thought (CoT) prompting approach, where the model reasons over failing tests, suspicious code regions, and prior patch outcomes, before generating new candidate patches. The model iteratively changes the code, evaluates the results, and incorporates reasoning from previous attempts into subsequent modifications, reducing repeated errors and clarifying why some bugs remain unresolved. Our evaluation spans 3,902 bugs from the Codeflaws benchmark, where our approach achieves 44.93% repair accuracy, representing a 3.61% absolute improvement over strong state-of-the-art APR baselines such as GPT-4 with CoT. This outcome highlights a practical pathway toward integrating statistical program analysis with generative AI in automated debugging.