Software Defect Prediction Through Effective Weighted Optimization Model for Assured Software Quality
Keywords:
Software Defect, Firefly Optimization, Feature Selection, Weighted FCM, ClassificationAbstract
Software Defect Prediction is one of the active research areas in software engineering. Defect prediction approach identifies the defect prone modules before the testing phase starts. Metrics based defect prone modules improve the software quality, reduce the cost and leading to effective allocation of resources. This paper developed an effective software defect prediction model for the software quality assurance. In the first module, the various classifier’s performance is analyzed using all the metrics of the KC1 dataset. In the second module, Firefly optimization algorithm is used for selecting the minimal number of metrics and passing them as input to the SVM classifier. In this paper, the fitness function of the Firefly algorithm is modified to maximize the accuracy and minimize the number of metrics. Based on the fitness function, Firefly algorithm tries to find a better set of metrics which improve the accuracy of defect prediction. In the third module, Hybrid FF or WFCMFF (Weighted FCM Firefly Search) approach is proposed to find a better set of metrics to further improve the performance of defect prediction. This approach combines the Firefly Algorithm and the Stochastic Weighted FCM Search algorithm to select the better set of metrics. The obtained results show that, the WFCMFF approach classifies the defect prone modules better when compared to the FF based feature selection. The achieved accuracy is 93.26%. for the SVM classifier. The classification-based defect prediction Model is evaluated in terms of its accuracy in classifying the module as defective or non-defective. Results proved that the proposed defect prediction Model has improved the accuracy from 86.27 % to 93.26%. Thus, the proposed classification-based defect prediction Model using FF and WFCMFF approaches, highly improves the defect prediction task.
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