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Scaling Token-Based Code Clone Detection

Project Dates: 
July 2011 to January 2014
Project Description: 

We developed a token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also developed a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrated how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.

In our experiments on 36 open source Java projects, we found that: (i) filtering reduces token comparisons by a factor of 10, and thus increasing the speed of clone detection by a factor of 1.5; (ii) the speed-up and scale-up of the parallel approach using filtering is near-linear on a cluster of 2-32 nodes for 150-2800 projects; and (iii) filtering decreases the memory usage of index-based approach by half and the search time by a factor of 5.​

This work was followed by SourcererCC: Scaling Type-3 Clone Detection to Large Software Repositories.

Publications: 
Sajnani, H., V. Saini, and C. Lopes, "A Parallel and Efficient Approach to Large Scale Clone Detection", Journal of Software: Evolution and Process, vol. 27, no. 6, pp. 402-429, Jun (online:Mar), 2015.
Sajnani, H., and C. V. Lopes, "A Parallel and Efficient Approach to Large Scale Code Clone Detection", International Workshop on Software Clones (IWSC 2013), held at ICSE 2013, San Francisco, CA, May, 2013.
Sajnani, H., J. Ossher, and C. V. Lopes, "Parallel Code Clone Detection Using MapReduce", International Conference on Program Comprehension 2012. Poster paper, Passau, Germany, June 11-13, 2012.