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Article type: Research Article
Authors: Juneja, Shallua; b; * | Bhathal, Gurjit Singha | Sidhu, Brahmaleen K.a
Affiliations: [a] Computer Science Engineering Department, Punjabi University, Patiala, India | [b] Computer Science Engineering Department, Maharaja Agrasen Institute of Technology, Delhi, India
Correspondence: [*] Corresponding author: Shallu Juneja, Computer Science Engineering Department, Punjabi University, Patiala, India. E-mail: shallujuneja9@gmail.com.
Abstract: Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony Optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.
Keywords: Fault prediction, classifiers, bio-inspired optimization algorithms, binary ant colony optimization (BACO) performance metrics
DOI: 10.3233/IDT-230427
Journal: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1355-1376, 2024
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