An Optimized Deep Learning Method for Software Defect Prediction Using Whale optimization Algorithm (WOA): A Review

Anes Aliyu Aihong, Badamasi Imam Ya'u, Usman Ali, Abuzairu Ahmad

Abstract


Software defect pose significant challenges in the development and maintenance of software systems, leading to increased costs and potential quality issues. In this research, we propose an innovative approach to enhance software defect prediction using an optimized deep learning method. We leverage the whale optimization algorithm (WOA) to fine-tune the parameters of our deep learning model, thereby improving its predictive accuracy. By incorporating the WOA, we achieve a more efficient and effective defect prediction model compared to traditional methods. Our experimental results demonstrate the superiority of our approach in terms of prediction accuracy and reliability, showcasing its potential for practical software quality assurance. This study contributes to the field of software engineering by providing a novel framework that leverages the synergy between deep learning and optimization techniques for software defect prediction. The developed optimized LSTM model will be implemented using Python 3.10. To estimate the prediction capabilities of our LSTM model, we used 19 open-source software defect datasets. These defect datasets are collected from the tera-PROMISE data repository. The computer to be used is a HP laptop running on Windows 10 Operating System with 8GB RAM and Pentium ® Core i7 processor.  


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