A systematic literature review on the use of machine learning in code clone research

Abstract

Context:Research related to code clones includes detection of clones in software systems, analysis, visualization and management of clones. Detection of semantic clones and management of clones have attracted use of machine learning techniques in code clone related research. Objective:The aim of this study is to report the extent of machine learning usage in code clone related research areas. Method:The paper uses a systematic review method to report the use of machine learning in research related to code clones. The study considers a comprehensive set of 57 articles published in leading conferences, workshops and journals. Results:Code clone related research using machine learning techniques is classified into different categories. Machine learning and deep learning algorithms used in the code clone research are reported. The datasets, features used to train machine learning models and metrics used to evaluate machine learning algorithms are reported. The comparative results of various machine learning algorithms presented in primary studies are reported. Conclusion:The research will help to identify the status of using machine learning in different code clone related research areas. We identify the need of more empirical studies to assess the benefits of machine learning in code clone research and give recommendations for future research.