Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality

Abstract

The quality and effectiveness of software systems may be significantly impacted by Software Defects (SD). Therefore, enhancing process quality is essential for controlling and minimizing the incidence of faults. Implementing reliable Software Development (SDe) processes and best practices is one way to do this information. SD, commonly referred to as software bugs or software mistakes, are defects or errors that occur in computer programs and cause them to act up or create unintended outcomes. These vulnerabilities may appear for several causes, including programming mistakes, poor design choices, or issues with the SDe cycle. The prediction of software problems based on Machine Learning (ML) using an Enhanced Artificial Neural Network (E-ANN) is implemented in this study to increase software quality and testing effectiveness. Particle swarm optimization and grey wolf optimization, these two algorithms named grey wolf swarm optimization algorithm (GWSA), are combined to recognize the corresponding compensation of the methods following the respective benefits and drawbacks. The hybrid algorithm-based model to the conventional hyperparameter optimization strategy and a single swarm intelligence algorithm's investigation of investigational consequences from six data sets shows that the hybrid algorithm-based model has high and enhanced indicators. Processed by the autoencoder, the model's performance has also improved.