KCoEvo: A Knowledge Graph Augmented Framework for Evolutionary Code Generation
This work tackles the practical challenge of maintaining code integrity for developers when third-party APIs evolve, offering an incremental improvement over existing LLM approaches.
The paper addresses the challenge of code evolution due to API changes by proposing a knowledge graph-augmented framework. This framework decomposes the task into evolution path retrieval and path-informed code generation, significantly improving migration accuracy, controllability, and execution success over standard LLM baselines.
Code evolution is inevitable in modern software development. Changes to third-party APIs frequently break existing code and complicate maintenance, posing practical challenges for developers. While large language models (LLMs) have shown promise in code generation, they struggle to reason without a structured representation of these evolving relationships, often leading them to produce outdated APIs or invalid outputs. In this work, we propose a knowledge graph-augmented framework that decomposes the migration task into two synergistic stages: evolution path retrieval and path-informed code generation. Our approach constructs static and dynamic API graphs to model intra-version structures and cross-version transitions, enabling structured reasoning over API evolution. Both modules are trained with synthetic supervision automatically derived from real-world API diffs, ensuring scalability and minimal human effort. Extensive experiments across single-package and multi-package benchmarks demonstrate that our framework significantly improves migration accuracy, controllability, and execution success over standard LLM baselines. The source code and datasets are available at: https://github.com/kangjz1203/KCoEvo.