# Improving LLM-Based Fault Localization with External Memory and Project Context

arXiv: https://arxiv.org/abs/2506.03585 · HTML: https://mlsanity.com/paper/2506.03585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures](https://mlsanity.com/paper/2512.05908/) (2025)
- [Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads](https://mlsanity.com/paper/2606.09956/) (2026)
- [How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring](https://mlsanity.com/paper/2606.26979/) (2026)

## Abstract (arXiv)

Fault localization, the process of identifying the software components responsible for failures, is essential but often time-consuming. Recent advances in Large Language Models (LLMs) have enabled fault localization without extensive defect datasets or model fine-tuning. However, existing LLM-based methods rely only on general LLM capabilities and lack integration of project-specific knowledge, resulting in limited effectiveness, especially for complex software. We introduce MemFL, a novel approach that enhances LLM-based fault localization by integrating project-specific knowledge via external memory. This memory includes static summaries of the project and dynamic, iterative debugging insights gathered from previous attempts. By leveraging external memory, MemFL simplifies debugging into three streamlined steps, significantly improving efficiency and accuracy. Iterative refinement through dynamic memory further enhances reasoning quality over time. Evaluated on the Defects4J benchmark, MemFL using GPT-4o-mini localized 12.7% more bugs than current LLM-based methods, achieving this improvement with just 21% of the execution time (17.4 seconds per bug) and 33% of the API cost (0.0033 dollars per bug). On complex projects, MemFL's advantage increased to 27.6%. Additionally, MemFL with GPT-4.1-mini outperformed existing methods by 24.4%, requiring only 24.7 seconds and 0.0094 dollars per bug. MemFL thus demonstrates significant improvements by effectively incorporating project-specific knowledge into LLM-based fault localization, delivering high accuracy with reduced time and cost.