# FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models

arXiv: https://arxiv.org/abs/2411.10714 · HTML: https://mlsanity.com/paper/2411.10714/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2024.

## Built on by (most cited first, 8 of 8)

- [Issue Localization via LLM-Driven Iterative Code Graph Searching](https://mlsanity.com/paper/2503.22424/) (2025)
- [Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models](https://mlsanity.com/paper/2504.04372/) (2025)
- [Agentic Code Reasoning](https://mlsanity.com/paper/2603.01896/) (2026)
- [LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis](https://mlsanity.com/paper/2601.05539/) (2026)
- [ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization](https://mlsanity.com/paper/2605.03117/) (2026)
- [Writing Bug Reports for Software Repair Agents: What Information Matters Most?](https://mlsanity.com/paper/2607.09553/) (2026)
- [SemLoc: Structured Grounding of Free-Form LLM Reasoning for Fault Localization](https://mlsanity.com/paper/2603.29109/) (2026)
- [Improving MPI Error Detection and Repair with Large Language Models and Bug References](https://mlsanity.com/paper/2604.02398/) (2026)

## Abstract (arXiv)

Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, we propose a novel LLM-based FL framework named FlexFL, which can flexibly leverage different types of bug-related information and effectively work with open-source LLMs. FlexFL is composed of two stages. In the first stage, FlexFL reduces the search space of buggy code using state-of-the-art FL techniques of different families and provides a candidate list of bug-related methods. In the second stage, FlexFL leverages LLMs to delve deeper to double-check the code snippets of methods suggested by the first stage and refine fault localization results. In each stage, FlexFL constructs agents based on open-source LLMs, which share the same pipeline that does not postulate any type of bug-related information and can interact with function calls without the out-of-the-box capability. Extensive experimental results on Defects4J demonstrate that FlexFL outperforms the baselines and can work with different open-source LLMs. Specifically, FlexFL with a lightweight open-source LLM Llama3-8B can locate 42 and 63 more bugs than two state-of-the-art LLM-based FL approaches AutoFL and AgentFL that both use GPT-3.5.