Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405
Abstract Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) algorithm with benchmark machine-learning classifiers… More >
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