A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

• Published in: International journal of intelligent computing and cybernetics Vol. 17; no. 2; pp. 436 - 464
• Main Authors: Mustaqeem, Mohd, Mustajab, Suhel, Alam, Mahfooz
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 30.05.2024; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Artificial neural networks; Collaboration; Datasets; Defects; Efficiency; Feature selection; Machine learning; Multilayer perceptrons; Optimization; Optimization techniques; Performance evaluation; Predictions; Quality assurance; Quality control; Software; Software quality; Multilayer perceptron; Feature selection; Hybrid approach; Grey wolf optimization; Software defect prediction
• ISSN: 1756-378X; 1756-3798
• DOI: 10.1108/IJICC-11-2023-0385

PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.