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

arXiv: https://arxiv.org/abs/2411.18015 · HTML: https://mlsanity.com/paper/2411.18015/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2024.

## Built on by (most cited first, 16 of 16)

- [Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving](https://mlsanity.com/paper/2504.02605/) (2025)
- [SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution](https://mlsanity.com/paper/2507.23348/) (2025)
- [Otter: Generating Tests from Issues to Validate SWE Patches](https://mlsanity.com/paper/2502.05368/) (2025)
- [PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases](https://mlsanity.com/paper/2509.25238/) (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)
- [AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent](https://mlsanity.com/paper/2602.03955/) (2026)
- [Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents](https://mlsanity.com/paper/2602.22764/) (2026)
- [SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks](https://mlsanity.com/paper/2506.10954/) (2025)
- [TDFlow: Agentic Workflows for Test Driven Development](https://mlsanity.com/paper/2510.23761/) (2025)
- [Agentic Software Issue Resolution with Large Language Models: A Survey](https://mlsanity.com/paper/2512.22256/) (2025)
- [Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice](https://mlsanity.com/paper/2511.07017/) (2025)
- [SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories](https://mlsanity.com/paper/2601.13713/) (2026)
- [AutoCodeSherpa: Symbolic Explanations in AI Coding Agents](https://mlsanity.com/paper/2507.22414/) (2025)
- [EvoOtter: Evolutionary Reproduction Test Generator](https://mlsanity.com/paper/2607.02854/) (2026)
- [XAgent: eXecution-guided Agentic AI for Effective Localization and Resolution of GitHub Issues](https://mlsanity.com/paper/2609.09769/) (2026)
- [SWEPatchTest: Evaluating Software Tests Beyond Reproduction](https://mlsanity.com/paper/2610.18004/) (2026)

## Abstract (arXiv)

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%.