Vol.29, No.3, 2021, pp.669-684, doi:10.32604/iasc.2021.018405
Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction
  • Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1
1 Faculty of Computing, Riphah International University, Islamabad, 45211, Pakistan
2 Department of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
* Corresponding Author: Summrina Kanwal. Email:
Received 07 March 2021; Accepted 12 April 2021; Issue published 01 July 2021
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 for the better detection of bugs in software modules. Our proposed methodology was tested and validated using 5 open-source National Aeronautics and Space Administration (NASA) data sets from the PROMISE repository: CM1, KC2, JM1, KC1 and PC1. Results were reported in terms of accuracy level and of an AUC with highest accuracy, namely, 94.82%. The results of our experiments indicate that the detection capability of benchmark classifiers can be improved by incorporating Opt-aiNet as a feature selection (FS) method.
Feature selection (FS); machine learning; optimized artificial immune networks (Opt-aiNet); software defect prediction (SDP); software metrics
Cite This Article
B. Mumtaz, S. Kanwal, S. Alamri and F. Khan, "Feature selection using artificial immune network: an approach for software defect prediction," Intelligent Automation & Soft Computing, vol. 29, no.3, pp. 669–684, 2021.
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