WOAFS: Feature Selection Using Whale Optimization Algorithm for Software Fault Prediction

Software fault prediction (SFP) is an important process in the field of software engineering, as it helps to identify potential defects in software systems before they occur. One approach to software fault prediction is to use feature selection techniques to identify the most important features that are likely to be associated with software faults. The proposed framework is focused on feature selection using Whale Optimization Algorithm (WOAFS). The WOAFS is then compared with Genetic Algorithm (GAFS), Particle Swarm Optimization (PSOFS), Differential Evolutionary (DEFS), and Ant Colony Optimization (ACOFS) in a performance comparison testing. The outcomes of the proposed framework are contrasted with those of other popular classification methods as Naive Bayes (NB), Linear Discriminant Analysis (LDA), K Nearest Neighbour (KNN), Decision Tree (DT) and Quadratic Discriminant Analysis (QDA). According to the findings of the trials, the suggested framework performed better than other current techniques like GAFS, PSOFS, DEFS, and ACOFS.