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• W4288770003 abstract "AbstractSoftware defect prediction (SDP) is the emerging research direction, which attains significant consideration among the advanced research community. The SDP is utilized to evaluate the reliability and quality of the software together with better allocation of limited testing resources. Numerous data mining methods are available in SDP for analyzing the source code, and developmental process and also for extracting critical metrics. The Deep Recurrent Neural Network (DNN) is utilized in this research for the prediction purpose, in which the hyperparameters of the classifier are effectively tuned by the crow search optimization. Further, to minimize the computational time most relevant features are selected with the help of the wrapper selection technique. The analysis is done using the PROMISE data set, based on the performance metrics such as specificity, sensitivity, and accuracy. The specificity, sensitivity, and accuracy of the proposed method are found to be 98.6248%, 93.5694%, and 92.0506% in accordance with the training percentage.KeywordsSoftware defect predictionFeature selectionDeep learningOptimizationSoftware development" @default.
• W4288770003 created "2022-07-30" @default.
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• W4288770003 date "2022-01-01" @default.
• W4288770003 modified "2023-09-25" @default.
• W4288770003 title "CrDrcnn: Design and Development of Crow Optimization-Based Deep Recurrent Neural Network for Software Defect Prediction" @default.
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• W4288770003 doi "https://doi.org/10.1007/978-3-031-12638-3_2" @default.
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