Search results

Software 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 Gray 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. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray 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.

The 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.

The 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.

Experimental 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.

This study aims to investigate differences between Islamic and conventional banks in Pakistan with respect to their operational efficiency, liquidity risk and asset quality. Importantly, in addition to full-fledged Islamic and conventional banks, this study also investigates a more recently emerged breed of hybrid banks, i.e. Islamic divisions of conventional banks.

Data for the period 2011–2020 was collected from financial reports of all full-fledged Islamic banks (5), Islamic banking divisions of conventional banks (8) and conventional banks (20) in Pakistan. Logistic regressions were designed to test the proposed hypotheses.

The findings suggest that full-fledged Islamic banks are operationally less efficient and experience higher liquidity risk than conventional banks. However, the asset quality of Islamic banks is better than that of conventional banks. Next, in the robustness analysis, the authors extended the sample size by adding the Islamic divisions (window) of the conventional banks; they found almost the same result except for efficiency which turned out to be non-significantly related to bank type.

The findings are beneficial for investors, depositors, consumers and bank management in understanding the financial features of such as efficiency, liquidity and liquidity risk that separate Islamic banks from conventional banks.

The findings of this study present a clear picture to bankers and practitioners about some financial features of banking systems and depict that Islamic banks are in need to improve their liquidity risk management practices to compete with conventional banks.