Cited by 23Software Engineering ResearchTopic ModelingSoftware Engineering Techniques and Practices

A3-CodGen: A Repository-Level Code Generation Framework for Code Reuse With Local-Aware, Global-Aware, and Third-Party-Library-Aware

Liao, Dianshu, Pan, Shidong, Sun, Xiaoyu, Ren, Xiaoxue, Huang, Qing, Xing, Zhenchang, Jin, Huan, Li, Qinying·2024·IEEE Transactions on Software Engineering

Abstract

LLM-based code generation tools are essential to help developers in the software development process. Existing tools often disconnect with the working context, i.e., the code repository, causing the generated code to be not similar to human developers. In this paper, we propose a novel code generation framework, dubbed$A^{3}$-CodGen, to harness information within the code repository to generate code with fewer potential logical errors, code redundancy, and library-induced compatibility issues. We identify three types of representative information for the code repository: local-aware information from the current code file, global-aware information from other code files, and third-party-library information. Results demonstrate that by adopting the$A^{3}$-CodGen framework, we successfully extract, fuse, and feed code repository information into the LLM, generating more accurate, efficient, and highly reusable code. The effectiveness of our framework is further underscored by generating code with a higher reuse rate, compared to human developers. This research contributes significantly to the field of code generation, providing developers with a more powerful tool to address the evolving demands in software development in practice.

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Crossref · 10.1109/tse.2024.3486195

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  • LongCodeZip consistently outperforms advanced RAG methods (A3-CodGen, cAST, RepoGenix, RLCoder) on Long Code Completion across SeedCoder and Claude-3.7-Sonnet.

    “As shown in Table VI, LongCodeZip consistently outperforms these advanced RAG methods across the most competitive open-source and closed-source models, SeedCoder and Claude-3.7-Sonnet.”

    From LongCodeZip: Compress Long Context for Code Language Models (2025) · cites this paper as [39]

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References 72 · Cited by 23

Authors
Liao, D., Pan, S., Sun, X., Ren, X., Huang, Q., Xing, Z., Jin, H., & Li, Q.
Affiliations
  • Australian National University (AU)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • Zhejiang University (CN)
  • Jiangxi Normal University (CN)
  • Jiangxi University of Technology (CN)
Venue
IEEE Transactions on Software Engineering
Published
2024-12
Volume
50
Issue
12
Pages
3369-3384
ISSN
  • 0098-5589 (ISSN-L)
  • 1939-3520
  • 2326-3881
OpenAlex
W4403722215
Cited by
23
References
72
Citations by year
2026: 11 · 2025: 10 · 2024: 2
FWCI
8.67
Type
journal article
Language
en
Open access
closed
Locations
  • IEEE Transactions on Software Engineering (publishedVersion)
  • ANU Open Research (Australian National University) (submittedVersion)
Funders
  • National Natural Science Foundation of China (62302437, 62262031, 62367003)
  • Distinguished Youth Fund Project of the Natural Science Foundation of Jiangxi Province (20242BAB23011)
  • Gan Po Talent Support Program-Academic and Technical Leaders Training Program in Major Disciplines of Jiangxi Province (20243BCE51007)
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Keywords
code generation, large language models, code reuse, third-party libraries, data fusion, code redundancy

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