Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach

Refactoring aims to improve the quality of software without altering its functional behaviors. Understanding developers’ refactoring activities is essential to improve software maintainability. The use of machine learning (ML) libraries and frameworks in software systems has significantly increased in recent years, making the maximization of their maintainability crucial. Due to the data-driven nature of ML libraries and frameworks, they often undergo a different development process compared to traditional projects. As a result, they may experience various types of refactoring, such as those related to the data. The state-of-the-art refactoring detection tools have not been tested in the ML technical domain, and they are not specifically designed to detect ML-specific refactoring types (e.g., data manipulation) in ML projects; therefore, they may not adequately find all potential refactoring operations, specifically the ML-specific refactoring operations. Furthermore, a vast number of ML libraries and frameworks are written in Python, which has limited tooling support for refactoring detection. PyRef, a rule-based and state-of-the-art tool for Python refactoring detection, can iden