Debasis Ganguly
ABSTRACT
We investigate an information retrieval (IR) based approach to source code plagiarism detection. The standard method plagiarism detection by extensively checking pairwise similarities between documents is not scalable to large collections of source code documents. To make the task of source code plagiarism detection fast and scalable in practice, we propose an IR based approach. In this method each document is treated as a pseudo-query which retrieves a list of documents which are potential candidate for containing plagiarised material in decreasing order of their similarity to the query. A threshold is then applied on the relative similarity decrement ratios to create a set of documents as potential cases of source-code reuse. Instead of treating a source code as an unstructured text document, we explore term extraction from the annotated parse tree of a source code and also make use of a field-based language model for indexing and retrieval of source code documents. Results confirm that source code parsing plays a vital role in improving the plagiarism prediction accuracy.
- A. Z. Broder. Identifying and filtering near-duplicate documents. In Combinatorial Pattern Matching, 11th Annual Symposium, CPM 2000, Montreal, Canada, June 21-23, 2000, pages 1--10, 2000. Google Scholar
Digital Library
- D. Chae, J. Ha, S. Kim, B. Kang, and E. G. Im. Software plagiarism detection: a graph-based approach. In 22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013, pages 1577--1580. Google ScholarDigital Library
- D.-K. Chae, S.-W. Kim, J. Ha, S.-C. Lee, and G. Woo. Software plagiarism detection via the static api call frequency birthmark. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13, pages 1639--1643, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- E. Flores, P. Rosso, L. Moreno, and E. Villatoro-Tello. PAN@FIRE: Overview of SOCO Track on the Detection of SOurce COde Re-use. In Sixth Forum for Information Retrieval Evaluation (FIRE 2014), Bangalore, India, 2014.Google Scholar
- D. Hiemstra. Using Language Models for Information Retrieval. PhD thesis, CTIT, AE Enschede, 2000.Google Scholar
Index Terms
- DCU@FIRE-2014: An Information Retrieval Approach for Source Code Plagiarism Detection
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