SourcererCC: Scaling Code Clone Detection to Big-Code

Abstract

Despite a decade of active research, there has been a marked lack in clone detection techniques that scale to large repositories for detecting near-miss clones. In this paper, we present a token-based clone detector, SourcererCC, that can detect both exact and near-miss clones from large inter-project repositories using a standard workstation. It exploits an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks: (1) a big benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (25K projects, 250MLOC) using a standard workstation.