Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

A program’s bug, fault, or mistake that results in unintended is known as a software defect fault. Software flaws are programming errors due to mistakes the requirements, architecture, source code. Finding and fixing bugs soon they arise crucial goal of development can be achieved various ways. So, selecting handful optimal subsets features from any dataset prime approach. Indirectly, classification performance improved through selection features. novel approach feature (FS) has been developed, which incorporates Golden Jackal Optimization (GJO) algorithm, meta-heuristic optimization technique draws on hunting tactics golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, Naive Bayes, will aid subset relevant fault prediction datasets. To evaluate accuracy we compare its other methods such FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), FSACO (Ant Colony Optimization). The result got FSGJO great for almost all cases. For many results, given higher accuracy. By utilizing Friedman Holm tests, determine statistical significance, suggested strategy verified found superior prior an set attributes.