KCoEvo: A Knowledge Graph Augmented Framework for Evolutionary Code Generation
Jiazhen Kang, Yuchen Lu, Chen Jiang, Jinrui Liu, Tianhao Zhang, Bo Jiang, Ningyuan Sun, Tongtong Wu, Guilin Qi

TL;DR
KCoEvo introduces a knowledge graph-augmented framework that enhances code evolution and API migration by modeling API changes and guiding code generation, significantly outperforming standard language models.
Contribution
The paper presents a novel framework combining static and dynamic API graphs with LLMs, improving code migration accuracy and scalability with minimal supervision.
Findings
Significant improvement in migration accuracy over LLM baselines
Effective modeling of intra- and cross-version API transitions
Scalable approach with minimal human supervision
Abstract
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struggle to reason without a structured representation of these evolving relationships, often leading them to produce outdated APIs or invalid outputs.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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Software Testing and Debugging Techniques · Advanced Software Engineering Methodologies
