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LLM-based code generation tools are essential to help developers in thesoftware development process. Existing tools often disconnect with the workingcontext, i.e., the code repository, causing the generated code to be notsimilar to human developers. In this paper, we propose a novel code generationframework, dubbed A^3-CodGen, to harness information within the code repositoryto generate code with fewer potential logical errors, code redundancy, andlibrary-induced compatibility issues. We identify three types of representativeinformation for the code repository: local-aware information from the currentcode file, global-aware information from other code files, andthird-party-library information. Results demonstrate that by adopting theA^3-CodGen framework, we successfully extract, fuse, and feed code repositoryinformation into the LLM, generating more accurate, efficient, and highlyreusable code. The effectiveness of our framework is further underscored bygenerating code with a higher reuse rate, compared to human developers. Thisresearch contributes significantly to the field of code generation, providingdevelopers with a more powerful tool to address the evolving demands insoftware development in practice.

A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware