AGENTFL: Scaling LLM-based Fault Localization to Project-Level Context

Core Concepts

AGENTFL introduces a multi-agent system for automated fault localization, enhancing Large Language Models (LLMs) to diagnose bugs at the project level.

Abstract

Abstract: AGENTFL addresses limitations of LLMs in fault localization by decomposing the process into comprehension, navigation, and confirmation stages. Introduction: FL is crucial but time-consuming; SBFL and LBFL are common techniques. LLMs show promise but struggle with long contexts. Approach: AGENTFL models FL as a three-step process using multiple agents with specialized expertise. Strategies like Test Behavior Tracking and Document-Guided Search are employed. Evaluation: AGENTFL outperforms LLM-based baselines in localizing bugs within Top-1 on Defects4J-V1.2.0 benchmark. Complementarity with existing techniques is observed. Experiment Design: Research questions focus on performance, design choices' impact, and practical usability through a user study.

Stats

AGENTFL can localize 157 out of 395 bugs within Top-1. AGENTFL spends an average of only 0.074 dollars and 97 seconds for a single bug.

Quotes

Key Insights Distilled From

AgentFL

by Yihao Qin,Sh... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16362.pdf

Deeper Inquiries

How can AGENTFL's approach be adapted for different programming languages?

AGENTFL's approach can be adapted for different programming languages by modifying the program analysis components to support the syntax and semantics of the target language. This would involve adjusting the static and dynamic analysis tools to work with the specific characteristics of the new language, such as method calls, class hierarchies, and code documentation formats. Additionally, prompts provided to LLM-driven agents would need to be tailored to accommodate the unique features of each programming language.

What ethical considerations should be taken into account when automating fault localization processes?

When automating fault localization processes like AGENTFL, several ethical considerations should be taken into account: Data Privacy: Ensure that sensitive information in codebases is protected during analysis. Bias Mitigation: Guard against biases in training data or model outputs that could impact decision-making. Transparency: Provide clear explanations for how faults are identified and localized by automated systems. Accountability: Establish mechanisms for accountability in case errors occur during fault localization. User Consent: Obtain consent from users before implementing automated fault localization tools on their projects.

How might the principles behind AGENTFL be applied to other software development tasks beyond fault localization?

The principles behind AGENTFL can be applied to various software development tasks beyond fault localization by adapting them to suit different objectives: Code Review Automation: Develop an automated system that reviews code changes based on predefined criteria using multi-agent collaboration similar to AGENTFL. Automated Documentation Generation: Implement a system that automatically generates comprehensive documentation for software components by leveraging LLM-driven agents with specialized expertise. Software Architecture Analysis: Create a tool that analyzes software architecture designs and provides recommendations based on multiple stages of comprehension, navigation, and confirmation akin to AGENTFL's approach. Automated Testing Frameworks Enhancement: Enhance testing frameworks with intelligent agents capable of identifying test cases or scenarios based on project-level context understanding. These applications demonstrate how the principles of multi-agent collaboration, task decomposition, and specialized agent capabilities employed in AGENTFL can be extended across various aspects of software development beyond just fault localization tasks.