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.