AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions

TL;DR

AEGIS is a novel agent-based framework that automates general bug reproduction from issue descriptions, significantly improving performance over existing methods by using structured context and feedback regulation.

Contribution

This paper introduces the first agent-based framework for general bug reproduction, combining context construction and FSM-based feedback regulation.

Findings

AEGIS outperforms state-of-the-art baselines by 23% in F->P metric.

Bug reproduction scripts from AEGIS increase the resolved rate of Agentless by 12.5%.

The framework effectively guides code agents to generate accurate bug reproduction scripts.

Abstract

In software maintenance, bug reproduction is essential for effective fault localization and repair. Manually writing reproduction scripts is a time-consuming task with high requirements for developers. Hence, automation of bug reproduction has increasingly attracted attention from researchers and practitioners. However, the existing studies on bug reproduction are generally limited to specific bug types such as program crashes, and hard to be applied to general bug reproduction. In this paper, considering the superior performance of agent-based methods in code intelligence tasks, we focus on designing an agent-based framework for the task. Directly employing agents would lead to limited bug reproduction performance, due to entangled subtasks, lengthy retrieved context, and unregulated actions. To mitigate the challenges, we propose an Automated gEneral buG reproductIon Scripts generation framework, named AEGIS, which is the first agent-based framework for the task. AEGIS mainly contains two modules: (1) A concise context construction module, which aims to guide the code agent in extracting structured information from issue descriptions, identifying issue-related code with detailed explanations, and integrating these elements to construct the concise context; (2) A FSM-based multi-feedback optimization module to further regulate the behavior of the code agent within the finite state machine (FSM), ensuring a controlled and efficient script generation process based on multi-dimensional feedback. Extensive experiments on the public benchmark dataset show that AEGIS outperforms the state-of-the-art baseline by 23.0% in F->P metric. In addition, the bug reproduction scripts generated by AEGIS can improve the relative resolved rate of Agentless by 12.5%.