Automatic Identification of Important Clones for Refactoring and Tracking

Code cloning is a controversial software engineering practice due to contradictory claims regarding its impacts on software evolution and maintenance. While a number of studies identify some positive aspects of code clones, there is strong empirical evidence of some negative impacts of clones too. Focusing on the issues related to clones researchers suggest to manage code clones through detection, refactoring, and tracking. However, all clones in a software system are not suitable for refactoring or tracking. Thus, it is important to identify which clones we should consider for refactoring and which clones should be considered for tracking. In this research work we apply the concept of evolutionary coupling to identify clones that are important for refactoring or tracking. By mining software evolution history, we determine and analyze constrained association rules of clone fragments that evolved following a particular change pattern called Similarity Preserving Change Pattern and are important from the perspective of refactoring and tracking. According to our investigation with rigorous manual analysis on thousands of revisions of six diverse subject systems covering two programming languages, overall 13.20% of all clones in a software system are important candidates for refactoring, and overall 10.27% of all clones are important candidates for tracking. Our implemented system can automatically identify these important candidates and thus, can help us in better maintenance of code clones in terms of refactoring and tracking.