MaintainCoder: Maintainable Code Generation Under Dynamic Requirements
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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
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- 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
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Citing papers per year: 2025: 2; 2026: 11.
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- 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Timecited by 3
- 2026Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Studycited by 1
- 2026SpecPath: Testing Coding Agents Across Contract-Equivalent Specification Historiescited by 0
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- 2026Do Coding Agents Reuse Existing Code or Reinvent the Wheel?cited by 0
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- 2026SpecPath: Testing Coding Agents Across Contract-Equivalent Specification Histories10 Aug 2026
- 2026SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades14 May 2026
- 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time1 Apr 2026
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks25 Mar 2026
- 2026Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study8 Feb 2026
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- 2026A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX28 Mar 2026
- 2026Controlled Self-Evolution for Algorithmic Code Optimization12 Jan 2026
- 2025CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation30 Apr 2025