Hybrid Neural Network with Weighted Modified Cuckoo Search Optimization for Software Defect Prediction: A Soft Computing Approach
Keywords:
Software Defect Prediction (SDP), Fuzzy C-means (FCM), Cuckoo Search (CS), Machine Learning, Hybrid Neural Network (HNN)Abstract
Defects in software significantly impact quality, reliability, and maintenance. Early detection and prediction using data mining and classification techniques offers an effective means of identifying potential defects before they manifest in production environments, but accurate prediction requires handling complex datasets. This paper proposes a soft computing model called Hybrid Neural Network with Weighted Modified Cuckoo Search Optimization (WMCSO) to detect the defect in the software. The proposed model first performs the clustering process with the Modified Fuzzy C–means algorithm (MFCM) to retrieve the important new attributes from the dataset. The software defect prediction and classification are performed using the HNN, and the WMCSO model is used to fine-tune the weights of the HNN. The HNN-WMCSO method is evaluated based on the evaluation of prediction rate and execution time. The experimental analysis stated that the proposed model exhibits improved performance relative to the current method in regard to an efficient prediction rate.
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