提交间细粒度源代码变更的系统文献综述：评估大规模数据收集的准备情况

IF 28 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-09-12 DOI:10.1145/3767321

Paweł Rzanny, Mirosław Ochodek

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引用次数: 0

摘要

细粒度代码变更（FGCC）记录工具提供了超越传统版本控制系统所提供的对代码演变的详细见解，支持训练现代机器学习模型所需的广泛数据收集。这个系统的回顾分析了92个主要的研究，发现大多数FGCC工具被设计用来支持对代码进化、开发人员协作或教育的研究，并且通常作为IDE插件或客户机-服务器应用程序实现。确定的主要挑战包括有限的互操作性、与特定技术紧密耦合导致的可持续性问题以及隐私问题。该审查建议制定标准化通信协议和数据模式，以改进FGCC工具集成，并促进人工智能应用的大规模数据收集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Systematic Literature Review of Inter-Commit Fine-Grained Source Code Changes: Assessing Readiness for Large-Scale Data Collection

Fine-grained code change (FGCC) recording tools provide detailed insights into code evolution beyond what traditional version control systems offer, supporting the extensive data collection required for training modern machine learning models. This systematic review analyzed 92 primary studies and found that most FGCC tools were designed to support research on code evolution, developer collaboration, or education and are typically implemented as IDE plugins or client-server applications. Key challenges identified include limited interoperability, sustainability issues due to tight coupling with specific technologies, and privacy concerns. The review recommends developing standardized communication protocols and data schemas to improve FGCC tool integration and facilitate large-scale data collection for AI applications.

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.