Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

• Published in: Mathematics (Basel) Vol. 11; no. 11; p. 2438
• Main Authors: Das, Himansu, Prajapati, Sanjay, Gourisaria, Mahendra Kumar, Pattanayak, Radha Mohan, Alameen, Abdalla, Kolhar, Manjur
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2023
• Subjects: Algorithms; Ant colony optimization; Classification; classification algorithms; Datasets; Debugging; Decision analysis; Decision trees; Defects; Discriminant analysis; Evolution & development; Evolutionary computation; Fault diagnosis; Feature selection; Foraging behavior; Genetic algorithms; golden jackal optimization; Heuristic methods; Machine learning; Mathematical optimization; Mutation; Optimization algorithms; Particle swarm optimization; Program errors; software defect prediction; Software development; software fault prediction; Software quality; Source code; Trends; Variables; Germany
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11112438

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