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In this paper, the authors presents a parallel and distributed data mining approach to code clone detection. It aims to prove the value and importance of deploying parallel and distributed computing for real-time large scale code clone detection. It is implemented this approach in a family of clone detectors, called PD EgyCD (Parallel and Distributed Egypt Clone Detector). This paper builds on an earlier work of the authors for code clone and plagiarism detection using sequential pattern mining by adding parallelism and distribution to their earlier tool EgyCD. Their approach uses data mining through a tailored Apriori-based algorithm for code clone detection. And it uses parallelization and distribution to achieve excellent performance to scale up to clone detection on very large systems.
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