Abstract
Software fault prediction models are very important to prioritize software classes for effective testing and efficient use of resources so that the testing process’s time, effort, and cost can be reduced. Fault prediction models can be based on either metrics’ threshold values or machine learning. Code metrics’ threshold-based models are easy to automate and faster than machine learning-based models, which can save significant time in the testing process. ROC, Alves ranking, and VARL are famous threshold value calculation techniques. Out of which ROC is the best threshold calculation technique. This research article proposes a new threshold values calculation technique based on metaheuristics. A genetic algorithm and particle swarm optimizer are used to calculate the threshold values, and the proposed technique is tested on ten open-source object-oriented software datasets and four open-source procedural software datasets. Results show that the metaheuristic-based thresholds give better results than ROC-based thresholds.
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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.
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Singh, M., Chhabra, J.K. Improved Software Fault Prediction Model Based on Optimal Features Set and Threshold Values Using Metaheuristic Approach. SN COMPUT. SCI. 4, 770 (2023). https://doi.org/10.1007/s42979-023-02217-x
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DOI: https://doi.org/10.1007/s42979-023-02217-x