# MarsCode Agent: AI-native Automated Bug Fixing

arXiv: https://arxiv.org/abs/2409.00899 · HTML: https://mlsanity.com/paper/2409.00899/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 26 of 26)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Practical Program Repair in the Era of Large Pre-trained Language Models](https://mlsanity.com/paper/2210.14179/) (2022)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Impact of Code Language Models on Automated Program Repair](https://mlsanity.com/paper/2302.05020/) (2023)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (2023)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [A Survey of Learning-based Automated Program Repair](https://mlsanity.com/paper/2301.03270/) (2023)
- [AgentFL: Scaling LLM-based Fault Localization to Project-Level Context](https://mlsanity.com/paper/2403.16362/) (2024)
- [Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models](https://mlsanity.com/paper/2301.03797/) (2023)
- [Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning](https://mlsanity.com/paper/2406.03718/) (2024)
- [DEAR: A Novel Deep Learning-based Approach for Automated Program Repair](https://mlsanity.com/paper/2205.01859/) (2022)
- [CCT5: A Code-Change-Oriented Pre-Trained Model](https://mlsanity.com/paper/2305.10785/) (2023)
- [Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches](https://mlsanity.com/paper/2405.03998/) (2024)
- [Enhancing Code Intelligence Tasks with ChatGPT](https://mlsanity.com/paper/2312.15202/) (2023)

## Built on by (most cited first, 30 of 71)

- [The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks](https://mlsanity.com/paper/2502.08235/) (2025)
- [Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?](https://mlsanity.com/paper/2511.13646/) (2025)
- [Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling](https://mlsanity.com/paper/2507.23370/) (2025)
- [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025)
- [SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks](https://mlsanity.com/paper/2506.11791/) (2025)
- [Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement](https://mlsanity.com/paper/2411.00622/) (2024)
- [SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution](https://mlsanity.com/paper/2507.23348/) (2025)
- [You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects](https://mlsanity.com/paper/2412.10133/) (2024)
- [OrcaLoca: An LLM Agent Framework for Software Issue Localization](https://mlsanity.com/paper/2502.00350/) (2025)
- [Repo2Run: Automated Building Executable Environment for Code Repository at Scale](https://mlsanity.com/paper/2502.13681/) (2025)
- [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025)
- [MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation](https://mlsanity.com/paper/2502.12468/) (2025)
- [Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks](https://mlsanity.com/paper/2505.16901/) (2025)
- [SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling](https://mlsanity.com/paper/2506.07636/) (2025)
- [Process-Centric Analysis of Agentic Software Systems](https://mlsanity.com/paper/2512.02393/) (2025)
- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification](https://mlsanity.com/paper/2502.02747/) (2025)
- [SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents](https://mlsanity.com/paper/2512.07850/) (2025)
- [Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks](https://mlsanity.com/paper/2508.13143/) (2025)
- [SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code](https://mlsanity.com/paper/2506.05692/) (2025)
- [An Empirical Study on Failures in Automated Issue Solving](https://mlsanity.com/paper/2509.13941/) (2025)
- [CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System](https://mlsanity.com/paper/2505.04254/) (2025)
- [Git Context Controller: Manage the Context of LLM-based Agents like Git](https://mlsanity.com/paper/2508.00031/) (2025)
- [A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System](https://mlsanity.com/paper/2510.09721/) (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)
- [Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents](https://mlsanity.com/paper/2602.07900/) (2026)
- [Co-PatcheR: Collaborative Software Patching with Component(s)-specific Small Reasoning Models](https://mlsanity.com/paper/2505.18955/) (2025)
- [Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing](https://mlsanity.com/paper/2506.16136/) (2025)
- [CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering](https://mlsanity.com/paper/2412.14764/) (2024)
- [LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues](https://mlsanity.com/paper/2411.13941/) (2024)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have shown significant potential to automate various software development tasks, including code completion, test generation, and bug fixing. However, the application of LLMs for automated bug fixing remains challenging due to the complexity and diversity of real-world software systems. In this paper, we introduce MarsCode Agent, a novel framework that leverages LLMs to automatically identify and repair bugs in software code. MarsCode Agent combines the power of LLMs with advanced code analysis techniques to accurately localize faults and generate patches. Our approach follows a systematic process of planning, bug reproduction, fault localization, candidate patch generation, and validation to ensure high-quality bug fixes. We evaluated MarsCode Agent on SWE-bench, a comprehensive benchmark of real-world software projects, and our results show that MarsCode Agent achieves a high success rate in bug fixing compared to most of the existing automated approaches.