mlsanityData to 7 Oct 2026

MaintainCoder: Maintainable Code Generation Under Dynamic Requirements

arXiv 2503.24260 ↗First posted 31 Mar 2025cs.SE cs.CLcited by 13

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Abstract (arXiv)

Modern code generation has made significant strides in functional correctness and execution efficiency. However, these systems often overlook a critical dimension in real-world software development: maintainability. To handle dynamic requirements with minimal rework, we propose MaintainCoder as a pioneering solution. It integrates the Waterfall model, design patterns, and multi-agent collaboration to systematically enhance cohesion, reduce coupling, achieving clear responsibility boundaries and better maintainability. We also introduce MaintainCoder, a benchmark comprising requirement changes and novel dynamic metrics on maintenance efforts. Experiments demonstrate that existing code generation methods struggle to meet maintainability standards when requirements evolve. In contrast, MaintainCoder improves dynamic maintainability metrics by more than 60% with even higher correctness of initial codes. Furthermore, while static metrics fail to accurately reflect maintainability and even contradict each other, our proposed dynamic metrics exhibit high consistency. Our work not only provides the foundation for maintainable code generation, but also highlights the need for more realistic and comprehensive code generation research. Resources: https://github.com/IAAR-Shanghai/MaintainCoder.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

25 of 48 references.

References · 25

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  4. 2024Qwen2.5 Technical Reportcited by 4,713
  5. 2021Program Synthesis with Large Language Modelscited by 3,509
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  7. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  8. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  9. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  10. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  11. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  12. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
13 more
  1. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  2. 2024A Survey on Large Language Models for Code Generationcited by 646
  3. 2023RepoBench: Benchmarking Repository-Level Code Auto-Completion Systemscited by 346
  4. 2023Self-collaboration Code Generation via ChatGPTcited by 255
  5. 2023Magicoder: Empowering Code Generation with OSS-Instructcited by 251
  6. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  7. 2024MapCoder: Multi-Agent Code Generation for Competitive Problem Solvingcited by 177
  8. 2024Self-Evolving Multi-Agent Collaboration Networks for Software Developmentcited by 73
  9. 2023A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trendscited by 66
  10. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  11. 2024EffiBench: Benchmarking the Efficiency of Automatically Generated Codecited by 62
  12. 2024HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generationcited by 39
  13. 2024Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Modelscited by 9

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Citing papers per year: 2025: 2; 2026: 11.

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