Contributions of Bioinformatics for computing education in the detection of programming assignment plagiarism
Resumo
Any source code can be modified in several ways to confuse plagiarism detection systems. Such diverse modifications require the usage of systems which can handle different types of plagiarism. The usage of automatic source code plagiarism detectors has implications for computing education. This paper extends Pedersen's work, a Bioinformatics method, by performing the application of this method on programming plagiarism domain, and by analyzing the usage of such tool through a discussion associated with the support for professors in assessing students' assignments. The application results are compared to a commonly used solution for the same purpose, the JPLAG tool. As a result of the evaluating study, the applied method showed a higher rate of similarity for specific types of plagiarism. Also, as a result of the analysis involving the use of an automatic tool for plagiarism in programming, showed the benefits for computing education.
Palavras-chave:
Bioinformatics, plagiarism, computing education, plagiarism detection, programming
Referências
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Chuda, D., Navrat, P., Kovacova, B., and Humay, P. (2012). The issue of (software) plagiarism: A student view. IEEE Transactions on Education, 55(1):22–28.
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Parker, A. and Hamblen, J. O. (1989). Computer algorithms for plagiarism detection. IEEE Transactions on Education, 32(2):94–99.
Pedersen, J., Bastola, D., Dick, K., Gandhi, R., and Mahoney, W. (2012). Blast your way through malware malware analysis assisted by bioinformatics tools. In Proceedings of the International Conference on Security and Management (SAM), page 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Prechelt, L., Malpohl, G., and Philippsen, M. (2002). Finding plagiarisms among a set of programs with jplag. J. UCS, 8(11):1016.
Ribeiro, M., Paes, R., Neto, B. S., Pereira, J. L., Castro, T., and Gheyi, R. (2018a). 30 days after introducing programming: Which of my students are likely to fail? In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 1283.
Ribeiro, R. B., Fernandes, D., de Carvalho, L. S. G., and Oliveira, E. (2018b). Gamificação de um sistema de juiz online para motivar alunos em disciplina de programação introdutória. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 805.
Roy, C. K. and Cordy, J. R. (2007). A survey on software clone detection research. Queen’s School of Computing TR, 541(115):64–68.
Sawyer, S., Horton, M., Burdyshaw, C., Brook, G., and Rekapalli, B. (2019). Hpc-blast: Distributed blast for modern hpc clusters. In Proceedings of 11th International Conference, volume 60, pages 1–14.
Sraka, D. and Kaucic, B. (2009). Source code plagiarism. In Information Technology Interfaces, 2009. ITI’09. Proceedings of the ITI 2009 31st International Conference on, pages 461–466. IEEE.
Stadelhofer, L. E. and Gasparini, I. (2018). Ensino de algoritmos e lógica de programação para os diferentes cursos: Um mapeamento sistemático da literatura. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 108.
Watson, J. D., Crick, F. H., et al. (1953). Molecular structure of nucleic acids. Nature, 171(4356):737–738.
Durić, Z. and Gašević, D. (2013). A source code similarity system for plagiarism detection. The Computer Journal, 56(1):70–86.
Arya, G. P., Bharti, R. K., Prasad, D., and Garg, V. (2017). An improved method for dna sequence compression. In 2017 2nd International Conference on Telecommunication and Networks (TEL-NET), pages 1–4.
Barbosa, R. and Souza, R. (2018). Um levantamento dos determinantes de inovação em softwares educacionais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 437.
Chuda, D., Navrat, P., Kovacova, B., and Humay, P. (2012). The issue of (software) plagiarism: A student view. IEEE Transactions on Education, 55(1):22–28.
Hunter, L. E. (2012). The processes of life: an introduction to molecular biology. Mit Press.
Jayapriya, J. and Arock, M. (2015). Pairwise local alignment using wavelet transform. In 2015 Annual IEEE India Conference (INDICON), pages 1–5.
Le Nguyen, T. T., Carbone, A., Sheard, J., and Schuhmacher, M. (2013). Integrating source code plagiarism into a virtual learning environment: benefits for students and staff. In Proceedings of the Fifteenth Australasian Computing Education Conference-Volume 136, pages 155–164. Australian Computer Society, Inc.
Mozgovoy, M., Kakkonen, T., and Cosma, G. (2010). Automatic student plagiarism detection: future perspectives. Journal of Educational Computing Research, 43(4):511– 531.
Novak, M. (2016). Review of source-code plagiarism detection in academia. In 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 796–801. IEEE.
Orabi, E. S., Assal, M. A., Azim, M. A., and Kamal, Y. (2014). Dna fingerprint using smith waterman algorithm by grid computing. In 2014 9th International Conference on Informatics and Systems, pages PDC–74–PDC–79.
Parker, A. and Hamblen, J. O. (1989). Computer algorithms for plagiarism detection. IEEE Transactions on Education, 32(2):94–99.
Pedersen, J., Bastola, D., Dick, K., Gandhi, R., and Mahoney, W. (2012). Blast your way through malware malware analysis assisted by bioinformatics tools. In Proceedings of the International Conference on Security and Management (SAM), page 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Prechelt, L., Malpohl, G., and Philippsen, M. (2002). Finding plagiarisms among a set of programs with jplag. J. UCS, 8(11):1016.
Ribeiro, M., Paes, R., Neto, B. S., Pereira, J. L., Castro, T., and Gheyi, R. (2018a). 30 days after introducing programming: Which of my students are likely to fail? In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 1283.
Ribeiro, R. B., Fernandes, D., de Carvalho, L. S. G., and Oliveira, E. (2018b). Gamificação de um sistema de juiz online para motivar alunos em disciplina de programação introdutória. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 805.
Roy, C. K. and Cordy, J. R. (2007). A survey on software clone detection research. Queen’s School of Computing TR, 541(115):64–68.
Sawyer, S., Horton, M., Burdyshaw, C., Brook, G., and Rekapalli, B. (2019). Hpc-blast: Distributed blast for modern hpc clusters. In Proceedings of 11th International Conference, volume 60, pages 1–14.
Sraka, D. and Kaucic, B. (2009). Source code plagiarism. In Information Technology Interfaces, 2009. ITI’09. Proceedings of the ITI 2009 31st International Conference on, pages 461–466. IEEE.
Stadelhofer, L. E. and Gasparini, I. (2018). Ensino de algoritmos e lógica de programação para os diferentes cursos: Um mapeamento sistemático da literatura. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 108.
Watson, J. D., Crick, F. H., et al. (1953). Molecular structure of nucleic acids. Nature, 171(4356):737–738.
Durić, Z. and Gašević, D. (2013). A source code similarity system for plagiarism detection. The Computer Journal, 56(1):70–86.
Publicado
11/11/2019
Como Citar
GOMES, Kaio P.; MATOS, Simone N..
Contributions of Bioinformatics for computing education in the detection of programming assignment plagiarism. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1351-1360.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1351.
