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Repository for the paper "Large Language Model-Based Agents for Software Engineering: A Survey". Keep updating.
| Date | Stars |
|---|---|
| 2026-07-31 | 552 |
| 2026-08-01 | 553 |
| 2026-08-06 | 553 |
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# Large Language Model-Based Agents for Software Engineering: A Survey
<p>
<a href="http://arxiv.org/pdf/2409.02977"><img src='https://img.shields.io/badge/ArXiv-Paper-purple'/></a>
<img src='https://img.shields.io/github/stars/FudanSELab/Agent4SE-Paper-List'/>
</p>
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain.
📍 **We systematically summarized the progress of Agent4SE from the perspectives of both *Software Engineering* tasks and *Agent Architecture*.**
:page_facing_up: Paper Link: [Large Language Model-Based Agents for Software Engineering: A Survey](http://arxiv.org/pdf/2409.02977)
<br/>
## :star: Star this repository
This research field is evolving rapidly; star this repository to keep up with the updates!
<br/>
## 📰 News
* **[2024/09/04]** 🎉 We released the first version of our survey on arXiv.
<br/>
## Table of Contents
* 📰 [News](#-news)
* 🖥️ [SE Perspectives](#%EF%B8%8F-se-perspectives)
* [Requirement Engineering](#requirement-engineering)
* [Code Generation](#code-generation)
* [Verification](#verification)
* [Static Code Checking](#static-checking)
* [Static Bug Detection](#static-bug-detection)
* [Code Review](#code-review)
* [Testing](#testing)
* [Unit Testing](#unit-testing)
* [System Testing](#system-testing)
* [Debugging](#debugging)
* [Fault Localization](#fault-localization)
* [Program Repair](#program-repair)
* [Unified Debugging](#unified-debugging)
* [Release](#release)
* [End-to-end Software Development](#end-to-end-software-development)
* [End-to-end Software Maintenance](#end-to-end-software-maintenance)
* 🤖[Agent Perspectives](#-agent-perspectives)
* [Agent Framework](#agent-framework)
* [Planning](#planning)
* [Memory](#memory)
* [Perception](#perception)
* [Action](#action)
* [Multi-agent System](#multi-agent-system)
* [Agent Roles](#agent-roles)
* [Collaboration Mechanism](#collaboration-mechanism)
* [Human-Agent Collaboration](#human-agent-collaboration)
* [Planning Phase](#planning-phase)
* [Requirements Phase](#requirements-phase)
* [Development Phase](#development-phase)
* [Evaluation Phase](#evaluation-phase)
* 📝 [Citation](#-citation)
* 👨🏻💻 [Maintainers](#-maintainers)
* 📬 [Contact Us](#-contact-us)
* 🌟 [Star History](#-star-history)
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## 🖥️ SE Perspectives
### Requirement Engineering
* [2024/05] **MARE: Multi-Agents Collaboration Framework for Requirements Engineering.** *Jin et al. arXiv.* [[paper](https://arxiv.org/pdf/2405.03256)]
* [2024/04] **Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation.** *Ataei et al. arXiv.* [[paper](https://arxiv.org/pdf/2404.16045)]
* [2024/01] **SpecGen: Automated Generation of Formal Program Specifications via Large Language Models.** *Ma et al. arXiv.* [[paper](https://arxiv.org/pdf/2401.08807)] [[repo](https://sites.google.com/view/specgen)]
* [2023/10] **Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs.** *Arora et al. arXiv.* [[paper](https://arxiv.org/pdf/2310.13976)]
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### Code Generation
* [2Excerpt of 105,014 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:70c6398b630cc67d, topic:large-language-models
matched fp:70c6398b630cc67d, name:paper list