An optimized deep learning method for software defect prediction using Whale Optimization Algorithm

Abstract

The goal of this study is to predict a software error using Long Short-Term Memory (LSTM). The suggested system is an LSTM taught using the Whale Optimization Algorithm to save training time while improving deep learning model efficacy and detection rate. MATLAB 2022a was used to develop the enhanced LSTM model. The study relied on 19 open-source software defect databases. These faulty datasets were obtained from the tera-PROMISE data collection. However, in order to evaluate the model performance to other traditional approaches, the scope of this study is limited to five (5) of the most highly ranked benchmark datasets (DO1, DO2, DO3, DO4, and DO5). The experimental results reveal that the quality of the training and testing data has a significant impact on fault prediction accuracy. As a result, when we look at the DO1 to DO5 datasets, we can see that prediction accuracy is significantly dependent on training and testing data. Furthermore, for DO2 datasets, the three deep learning algorithms tested in this study had the highest accuracy. The proposed method, however, outperformed Li’s and Nevendra’s two classical Convolutional Neural Network algorithms which attained accuracy of 0.922 and 0.942 on the DO2 software defect data, respectively.

Keywords

• deep learning
• software defect prediction
• whale optimization algorithms
• long short-term memory
• machine learning
• optimization algorithm