Classification model for code clones based on machine learning

Jiachen Yang, Keisuke Hotta, Yoshiki Higo, Hiroshi Igaki, Shinji Kusumoto

Abstract: Results from code clone detectors may contain plentiful useless code clones, but judging whether each code clone is useful varies from user to user based on a user’s purpose for the clone. In this research, we propose a classification model that applies machine learning to the judgments of each individual user regarding the code clones. To evaluate the proposed model, 32 participants completed an online survey to test its usability and accuracy. The result showed several important observations on the characteristics of the true positives of code clones for the users. Our classification model showed more than 70 % accuracy on average and more than 90 % accuracy for some particular users and projects.