Detection of source code plagiarism is valuable for both the academia and industry. Plagiarism is an approach of unlawfully stealing other person source code or program code which is a serious issue for common open source programming and other software companies. Numerous techniques have been introduced priori for automatic detection of source code plagiarism using Evolutionary Intelligent algorithm like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. These techniques are more susceptible to premature convergence and more time consuming. In this paper, considering the benefits of artificial immune system, source code plagiarism approach is proposed that overcomes the drawbacks of previous genetic algorithm and particle swarm optimization algorithms. The sparse PCA is employed for dimensionality reduction prior to detection approach for obtained sparse matrix. Using CSA, the detection between source codes is computed and fitness evaluation is measured using Normalized Euclidean distance (NED) and Normalized Cumulative Reciprocal Rank (NCRR).The performance analysis of the suggested approach showed that it has better precision and recall values when compared with existing Meta heuristic based Source code plagiarism detection algorithms.

Source Code detection, Plagiarism approach, Artificial Immune System, Clonal Selection Algorithm, Sparse PCA

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