Refine your search
Collections
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Almu, Abba
- Enhanced Semantic Similarity Detection of Program Code Using Siamese Neural Network
Abstract Views :112 |
PDF Views:0
Authors
Affiliations
1 Kofar kaura layout kastina, State, NG
2 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
3 Ministry of Finance Economic and Development, Damaturu, Yobe State, NG
1 Kofar kaura layout kastina, State, NG
2 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
3 Ministry of Finance Economic and Development, Damaturu, Yobe State, NG
Source
International Journal of Advanced Networking and Applications, Vol 14, No 2 (2022), Pagination: 5353-5360Abstract
Even though there are various source code plagiarism detection approaches, most of them are only concerned with lexical similarities attack with an assumption that plagiarism is only conducted by students who are not proficient in programming. However, plagiarism is often conducted not only due to student incapability but also because of bad time management. Thus, semantic similarity attacks should be detected and evaluated. This research proposes a source code semantic similarity detection approach that can detect most source code similarities by representing the source code into an Abstract Syntax Tree (AST) and evaluating similarity using a Siamese neural network. Since AST is a language-dependent feature, the SOCO dataset is selected which consists of C++ program codes. Based on the evaluation, it can be concluded that our approach is more effective than most of the existing systems for detecting source code plagiarism. The proposed strategy was implemented and an experimental study based on the AI-SOCO dataset revealed that the proposed similarity measure achieved better performance for the recommendation system in terms of precision, recall, and f1 score by 15%, 10%, and 22% respectively in the 100,000 datasets. In the future, it is suggested that the system can be improved by detecting inter-language source code similarity.Keywords
Source Code, Lexical plagiarism, Semantic neural network.References
- A. Alex. MOSS (Measure of software similarity) plagiarism detection system 1994. Retrieved from http:/www.cs.berkely.edu/~moss/. University of Berkely, CA.
- I. Baxter, A. Yahin, L. Moura, M. Anna, and L. Bier. Clone detection using abstract syntax trees. IEEE. Published in the Proceedings of International conference on software maintenance (ICSM’98): 1998.pp 368-377.
- B. N. Pellin. Using classification techniques to determine source code authorship. White paper: department of computer science, university of Wisconsin. 2000.
- D. Zou, W. Long, and Z. Ling. A cluster-based plagiarism detection method - Lab report for PAN at CLEF In Proceedings of the 4th Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse 2010.
- J. Son, S. Park, and S. Park. Program Plagiarism Detection Using Parse Tree Kernels. PRICAI’06 Proceedings of the 9th Pacific Rim international conference on artificial intelligence: 2006. pp 1000– 1004.
- C. Liu, C. Chen, J. Han and P. S. Yu. GPLAG: Detection of Software Plagiarism by Program Dependence Graph Analysis. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 2006. pp 872- 881.
- S. Harihan. Automatic Plagiarism Detection Using Similarity Analysis. The International Arab Journal of Information Technology: Vol. 9, issue 4. 2012.
- Z. Duric, and D. Gasevic. A Source Code Similarity System for Plagiarism Detection. The Computer Journal: Vol. 56, issue 1, 2012. pp 70-86.
- L. Zhang, D. Liu, Y. Li, and M. Zhong. AST-Based Plagiarism Detection Method. In: Wang Y., Zhang X. (eds) Internet of Things. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, Vol. 312. 2012.
- D. Ganguly, G. J. F. Jones, A. Ramı´rez-de-la-Cruz, G. Ramı ´rez-de-la-Rosa, and E. Villatoro-Tello. Retrieving and classifying instances of source code plagiarism: Information Retrieval journal: 2017. pp 1- 23.
- E. Flores, A. Barro ´n-Ceden ˜o, P. Rosso, and L. Moreno. Towards the detection of cross-language source code reuse. In Proceedings of the 16th international conference on applications of natural language to information systems: 2011. pp. 250–253.
- S. Narayanan and S. Simi. Source Code Plagiarism Detection and Performance Analysis Using Fingerprint Based Distance Measure Method. In Proceedings of 7th International Conference on Computer Science Education ICCSE ’12. IEEE: pp 1065-1068.
- R. Marinescu. Accessing Technical Debt by Identifying Design Flaws in Software Systems. IBM Journal of Research and Development: Vol. 56(5), 2012. pp 1-9.
- S. Ion and I. Bogdan. Source Code Plagiarism Detection Method Using Protégé Built Ontologies: Informatics EconomicsJournal: Vol. 17, 2013. pp 75- 86.
- T. Ohmann and I. Rahal. Efficient clustering-based source code plagiarism detection using PIY. Journal of Knowledge and Information Systems: vol. 43, 2014. pp 445-447.
- J. Zhao, K. Xia, Y. Fu, and B. Cui. An AST-Based Code Plagiarism Detection Algorithm: 10th International Conference on Broadband and Wireless Computing, Communication and Application. 2015.
- N. More, A. A. Bhootra and C. A. Patel. Plagiarism Detection in Source Code. IJIRST –International Journal for Innovative Research in Science & Technology: Volume 1, Issue 10 | March 2015 ISSN (online): 2349-6010, 2015. pp 109-112.
- N. Shah, S. Modha, and d. Dave. Differential Weight Based Hybrid Approach to Detect Software Plagiarism. In Proceedings of International Conference on ICT for Sustainable Development: Vol. 409, 2016. pp 645-653.
- O. Karnalim. Detecting Source Code Plagiarism on Introductory Programming Course Assignments Using a Bytecode Approach. The 10th International Conference on Information, Communication Technology and System (ICTS), Surabaya, Indonesia: IEEE, 2016. pp 63-68.
- O. Karnalim. A Low-Level Structure-based Approach for Detecting Source Code Plagiarism. IAENG International Journal of Computer Science: volume 44, 2017. pp 4.
- M. Duracik, E. Kirsak, and P. Hrkut. Source Code Representations for Plagiarism Detection. Springer International Publishing AG, part of Springer Nature: CCIS 870, 2018. pp 61–69.
- M. Duracik, E. Kirsak, and P. Hrkut. Scalable Source Code Plagiarism Detection Using Source Code Vectors Clustering. IEEE Journal: 2018. pp 7-18
- O. Karnalim. Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation. Jornal of Informatics in Education: Vol 18(2), 2019. pp 321-344.
- Dr. R, Kulkarni1 and K., Apana. A Novel Approach to Restructure the Input Java Program. Journal of advanced networking and applications: Volume: 12 Issue: 04 Pages: 4621-4626(2021).
- R. Kulkarni and P., Pani. Abstraction of UML Class Diagram from the Input Java Program. Journal of advanced networking and applications: Volume: 12 Issue: 04 Pages: 4644-4649(2021).
- Enhancing Personalized Book Recommender System.
Abstract Views :105 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto., NG
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto., NG
Source
International Journal of Advanced Networking and Applications, Vol 14, No 3 (2022), Pagination: 5486-5492Abstract
Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computesdocument similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. Th e performance of the proposed scheme was evaluated against the benchmark scheme usingdifferent performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.Keywords
Recommender System, Content-Based, Collaborative Filtering, Personalized Recommendations, Similarity Function.References
- M. Prem, and S. Vikas. Recommender Systems. (Encyclopedia of Machine Learning), 2010.
- C. Pan, and W. Li. Research paper recommendation with topic analysis. In computer design and Application IEEE 2010, 4-264.
- P. Pu, L. Chen, and R, Hu. A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender Systems(RecSys’11), ACM, New York, NY, USA; 2010, P. 57-164.
- S. Sanjeevan, S. Alireza, R. Hossein, and M. Asad. Recommender systems in e-commerce. In Proceedings of the World Automation Congress (WAC). IEEE, 2014, 179–184
- H. Kang, and J. Seong, SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems. IEICE Transactions on Information and Systems, 2007, 2100 –2103.
- L. George, and C. Petros. A hybrid approach for movie recommendation. Multimedia Tools and Applications. Conference on Innovative Applications of Artificial Intelligence2008, 55– 70.
- J. Bobadilla, F. Ortega, A. Hernando, and A. GutiéRrez. Recommender systems survey. Knowledge-Based System, 9(46): 2013, 109 -132.
- H. Zamani, and A. Shakery. A language model-based framework for multi-publisher content-based recommender systems. Forspringer International Journal on Information Retrieval,2(1): 2018, 369-409.
- U. Hanani, B. Shapira, and P. Shoval. Information filtering: Overview of issues, research, and systems. User Modeling and User-Adapted Interaction, 11(3), 2001, 203–259.
- P. Lops, M. Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In information Retrieval.2011, 73–105, Springer.
- S. Gong and J. Softw. A Collaborative Filtering Recommendation Algorithm Based On User Clustering and Item Clustering.Conference on Innovative Applications of Artificial Intelligence. 2010, 745-752.
- J. Sarun and K. Paween. Automatic non-personalized book recommender algorithm for Bookstore shelf management: The 3rd International Conference on Digital Arts, Media, and Technology (ICDAMT IEEE): 2018, 1-5.
- R. Chhavirana and K. J. Sanjay. Building a Book Recommender system using time-based Content Filtering. WSEAS Transactions on Computers Issue 2(11): 2012, 49-78.
- M. Suthathip and M. Songrit. A recommendation model for personalized book lists. In Communications and Information Technologies (ISCIT) International Symposium on IEEE,2010, 389-394.
- P. Jomsri. Book recommendation system for digital library based on user profiles by using association rule. In Innovative Computing Technology (INTECH), Fourth International Conference on IEEE, Luton., England: 2014, 130-134.
- J. Chen, C.U. Zhao, C. Chen. Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering” Complex & Intelligent Systems published online by Springer 30 January, 2019, 147-156.
- H. Xia, Y. Luo, and Y. Liu. Attention Neural Collaboration Filtering Base on GRU for Recommender Systems.” Complex and Intelligent Systems. Published @ Springer. 2020
- X. Liao, X. Li, Q. Xu, H. Wu, and Y. Wang. Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction”Applied Science. 2021, 72-45 http://www.mdpi.com/journal/applsci
- J. McAuley, and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7 th ACM conference on recommender systems. 7(13): 2013, 165-172.
- P. Priyanga, and A. R. N. B. Kamal. Methods of Mining the Data from Big Data and Social Networks Based on Recommender System. International Journal of Advanced Networking & Applications. 8(5): 2017, 55-60.