Abstract
A program consists of data structures and algorithms. However, most studies, up to now, for detecting plagiarism of source codes are suggesting lopsided analyses considering only the algorithms (or instructions) of the source codes. This paper introduces a method for measuring the similarity between data structures for detecting plagiarized source codes. The proposed method was experimented with test sets including plagiarized source codes. The experimental result shows that the similarities among the data structures of plagiarized source codes are high degree as expected. This result implies that the similarity on data structures, along with the similarity on algorithms, is also one of the main factors to the decrease false alarms by lowering the threshold for the plagiarism.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Roy, C.K., Cordy, J.R., Koschke, R.: Comparison and evaluation of code clone detection techniques and tools: a qualitative approach. Sci. Comput. Program. 74(7), 470–495 (2009)
Bellon, S., Koschke, R., Antonio, G., Krinke, J., Merlo, E.: Comparison and evaluation of clone detection tools. IEEE Trans. Softw. Eng. 33(9), 577–591 (2007)
Lee, Y., Lim, J., Ji, J., Cho, H., Woo, G.: Plagiarism detection among source codes using adaptive methods. Trans. Internet Inf. Syst. 6(6), 1627–1648 (2012)
Daly, C., Horgan, J.: A technique for detecting plagiarism in computer code. Comput. J. 48(6), 662–666 (2005)
Ji, J., Woo, G., Cho, H.: A source code linearization technique for detecting plagiarized programs. In: ACM SIGCSE Bulletin, vol. 39, no. 3, pp. 73–77. ACM, New York (2007)
Ji, J. Woo, G., Park, S., Cho, H.: An intelligent system for detecting source code plagiarism using a probabilistic graph model. In: Machine Learning and Data Mining in Pattern Recognition Posters, pp. 55–69 (2007)
Chilowicz, M., Duris, E., Rousscl, G.: Syntax tree fingerprinting for source code similarity detection, In: 17th IEEE International Conference on Program Comprehension, pp. 243–247. IEEE (2009)
Ottenstein, K.J.: An algorithmic approach to the detection and prevention of plagiarism. ACM SIGCSE Bull. 8(4), 30–41 (1976)
Ji, J.: Program Similarity analysis framework using adaptive sequence alignment technique. Ph.D. thesis, Pusan National University (2010)
Ducasse, S., Nierstrasz, O., Rieger, M.: On the effectiveness of clone detection by string matching. J Softw. Maintenance Evol. Res. Pract. 18(1), 37–58 (2006)
Falke, R., Frenzel, P., Koschke, R.: Empirical evaluation of clone detection using syntax suffix trees. Empirical Softw. Eng. 13(6), 601–643 (2008)
Son, J., Park, S., Park, S.: Program plagiarism detection using parse tree kernels, In: Pacific Rim International Conference on Artificial Intelligence 2006: Trends in Artificial Intelligence, pp. 1000–1004. Springer Berlin Heidelberg (2006)
Jiang, L., Misherghi, G., Su, Z., Glondu, S.: Deckard: Scalable and accurate tree-based detection of code clones, In: 29th international conference on software Engineering, pp. 96—105. IEEE Computer Society, Washington DC (2007)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Kuhn, H.W.: Variants of the Hungarian method for the assignment problem. Naval Res. Logistics Q. 3(4), 253–258 (1956)
Acknowledgements
This work was supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lee, K., Kim, Y., Woo, G. (2019). Measuring Similarity Between Data Structures for Detecting Plagiarized Source Codes. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-1799-6_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1797-2
Online ISBN: 978-981-13-1799-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)