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  • Open Access


    Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

    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 >

  • Open Access


    Code Smell Detection Using Whale Optimization Algorithm

    Moatasem M. Draz1, Marwa S. Farhan2,3,*, Sarah N. Abdulkader4,5, M. G. Gafar6,7

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1919-1935, 2021, DOI:10.32604/cmc.2021.015586

    Abstract Software systems have been employed in many fields as a means to reduce human efforts; consequently, stakeholders are interested in more updates of their capabilities. Code smells arise as one of the obstacles in the software industry. They are characteristics of software source code that indicate a deeper problem in design. These smells appear not only in the design but also in software implementation. Code smells introduce bugs, affect software maintainability, and lead to higher maintenance costs. Uncovering code smells can be formulated as an optimization problem of finding the best detection rules. Although researchers have recommended different techniques to… More >

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