Threshold-free Code Clone Detection for a Large-Scale Heterogeneous Java Repository

Code clones are unavoidable entities in software ecosystems. A variety of clone-detection algorithms are available for finding code clones. For Type-3 clone detection at method granularity (i.e., similar methods with changes in statements), dissimilarity threshold is one of the possible configuration parameters. Existing approaches use a single threshold to detect Type-3 clones across a repository. However, our study shows that to detect Type-3 clones at method granularity on a large-scale heterogeneous repository, multiple thresholds are often required. We find that the performance of clone detection improves if selecting different thresholds for various groups of clones in a heterogeneous repository (i.e., various applications). In this paper, we propose a threshold-free approach to detect Type-3 clones at method granularity across a large number of applications. Our approach uses an unsupervised learning algorithm, i.e., k-means, to determine true and false clones. We use a clone benchmark with 330,840 tagged clones from 24,824 open source Java projects for our study. We observe that our approach improves the performance significantly by 12% in terms of F-measure. Furthermore, our threshold-free approach eliminates the concern of practitioners about possible misconfiguration of Type-3 clone detection tools.