# AgentFL: Scaling LLM-based Fault Localization to Project-Level Context

arXiv: https://arxiv.org/abs/2403.16362 · HTML: https://mlsanity.com/paper/2403.16362/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 30 of 47)

- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [LocAgent: Graph-Guided LLM Agents for Code Localization](https://mlsanity.com/paper/2503.09089/) (2025)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration](https://mlsanity.com/paper/2406.01422/) (2024)
- [SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution](https://mlsanity.com/paper/2507.23348/) (2025)
- [Reducing Cost of LLM Agents with Trajectory Reduction](https://mlsanity.com/paper/2509.23586/) (2025)
- [OrcaLoca: An LLM Agent Framework for Software Issue Localization](https://mlsanity.com/paper/2502.00350/) (2025)
- [SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://mlsanity.com/paper/2601.16746/) (2026)
- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [Agentic Bug Reproduction for Effective Automated Program Repair at Google](https://mlsanity.com/paper/2502.01821/) (2025)
- [SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs](https://mlsanity.com/paper/2504.14757/) (2025)
- [SweRank: Software Issue Localization with Code Ranking](https://mlsanity.com/paper/2505.07849/) (2025)
- [An Empirical Study on Failures in Automated Issue Solving](https://mlsanity.com/paper/2509.13941/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion](https://mlsanity.com/paper/2409.13642/) (2024)
- [Can Agents Fix Agent Issues?](https://mlsanity.com/paper/2505.20749/) (2025)
- [AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generation](https://mlsanity.com/paper/2504.04220/) (2025)
- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering](https://mlsanity.com/paper/2411.15501/) (2024)
- [CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2504.13472/) (2025)
- [HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutions](https://mlsanity.com/paper/2507.21485/) (2025)
- [RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models](https://mlsanity.com/paper/2601.18044/) (2026)
- [Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair](https://mlsanity.com/paper/2412.03905/) (2024)
- [Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects](https://mlsanity.com/paper/2502.02368/) (2025)
- [SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization](https://mlsanity.com/paper/2506.20081/) (2025)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [Agentic Code Reasoning](https://mlsanity.com/paper/2603.01896/) (2026)
- [Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization](https://mlsanity.com/paper/2412.18750/) (2024)

## Abstract (arXiv)

Fault Localization (FL) is an essential step during the debugging process. With the strong capabilities of code comprehension, the recent Large Language Models (LLMs) have demonstrated promising performance in diagnosing bugs in the code. Nevertheless, due to LLMs' limited performance in handling long contexts, existing LLM-based fault localization remains on localizing bugs within a small code scope (i.e., a method or a class), which struggles to diagnose bugs for a large code scope (i.e., an entire software system). To address the limitation, this paper presents AgentFL, a multi-agent system based on ChatGPT for automated fault localization. By simulating the behavior of a human developer, AgentFL models the FL task as a three-step process, which involves comprehension, navigation, and confirmation. Within each step, AgentFL hires agents with diversified expertise, each of which utilizes different tools to handle specific tasks. Particularly, we adopt a series of auxiliary strategies such as Test Behavior Tracking, Document-Guided Search, and Multi-Round Dialogue to overcome the challenges in each step. The evaluation on the widely used Defects4J-V1.2.0 benchmark shows that AgentFL can localize 157 out of 395 bugs within Top-1, which outperforms the other LLM-based approaches and exhibits complementarity to the state-of-the-art learning-based techniques. Additionally, we confirm the indispensability of the components in AgentFL with the ablation study and demonstrate the usability of AgentFL through a user study. Finally, the cost analysis shows that AgentFL spends an average of only 0.074 dollars and 97 seconds for a single bug.