Artificial Intelligence Methods in Software Refactoring: A Systematic Literature Review

Refactoring is an important process in software engineering, aiming to improve code quality without altering the behavior. This article presents a systematic review of the literature (SLR) on artificial intelligence in the domain of software refactoring. Following a rigorous methodology consisting of data extraction, snowballing techniques, and manual validation, we created a dataset consisting of 156 articles. The focus of the investigation was to identify the refactoring stages that are addressed. The results show that, as research type, most of the contributions propose solutions, while other forms of research such as evaluation, validation and experience are less represented in publications. Refactoring detection represents the highest interest in research contributions, while other refactoring stages, such as prioritization or testing are less investigated. The most commonly used AI methods include Random Forests, Genetic Algorithms, SVM, CNNs and Decision Trees. Based on this literature review, we have identified research trends and opportunities for future research.