Particle swarm optimisation-based source code plagiarism detection approach using non-negative matrix factorisation algorithm

Abstract

Source code plagiarism is easy to do the task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Numerous efforts have been made in the literature to introduce an efficient source code detection approach with less time complexity and accurate classification of plagiarised codes. However, there exists a tradeoff amongst the less complexity and high accuracy. In a similar way, this paper likewise attempted to build a framework to detect the plagiarised codes from the source code corpus. This approach employed an intelligent swarm optimisation algorithm known as PSO in the detection phase and robust matrix factorisation algorithm known as non-negative matrix factorisation based on alternative least square (ALS) algorithm for reduction of features from the sparse matrix. Depending on the implementation, ALS is very fast and significantly less work than an SVD implementation. The experimental results showed that it has good performance compared to the other existing approaches such as precision and recall.