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Karimi, A., Irajimoghaddam, M., Bastami, E. (2022). Feature selection using a combination of Genetic-Whale-Ant colony algorithms for software fault prediction by machine learning. Electronic and Cyber Defense, 10(1), 33-45.
Ali Karimi; Mohammadreza Irajimoghaddam; Esmaeil Bastami. "Feature selection using a combination of Genetic-Whale-Ant colony algorithms for software fault prediction by machine learning". Electronic and Cyber Defense, 10, 1, 2022, 33-45.
Karimi, A., Irajimoghaddam, M., Bastami, E. (2022). 'Feature selection using a combination of Genetic-Whale-Ant colony algorithms for software fault prediction by machine learning', Electronic and Cyber Defense, 10(1), pp. 33-45.
Karimi, A., Irajimoghaddam, M., Bastami, E. Feature selection using a combination of Genetic-Whale-Ant colony algorithms for software fault prediction by machine learning. Electronic and Cyber Defense, 2022; 10(1): 33-45.

Feature selection using a combination of Genetic-Whale-Ant colony algorithms for software fault prediction by machine learning

پدافند الکترونیکی و سایبری
Article 4, Volume 10, Issue 1 - Serial Number 37, May 2022, Pages 33-45    PDF (1000.26 K)
Document Type: Original Article
Authors
Ali Karimi* 1; Mohammadreza Irajimoghaddam2; Esmaeil Bastami3
1Assistant Professor, Imam Hossein University, Tehran, Iran
2Master's degree, Imam Hossein University (AS), Tehran, Iran
3Researcher, Imam Hossein University, Tehran, Iran
Receive Date: 02 May 2021,  Revise Date: 12 July 2021,  Accept Date: 27 August 2021 
Abstract
Software fault prediction methods are used to predict fault-prone modules in the early stages of software development. Machine learning techniques are the most common techniques used in software fault prediction. Data dimensionality is one of the problems that affect the performance of machine learning algorithms. Data dimensionality means the existence of irrelevant or redundant features that may mislead the learning algorithm hence decrease its accuracy. The low accuracy of software fault prediction causes the late detection of some faulty modules and as a result increases the effort and cost of fixing faults abnormally. Therefore, solving the data dimensionality problem is necessary to increase the accuracy of software fault prediction. Researchers use the feature selection algorithms for dimensionality reduction. Feature selection algorithms are divided into two types of filter-based feature selection and wrapper-based feature selection algorithms. Wrapper-based algorithms lead to higher accuracy prediction models. In these algorithms we can use different methods to search for the good solutions; the best of which, is the metaheuristic search. Each of the metaheuristic algorithms has some strengths and weaknesses, so the researchers use a combination of these algorithms to address these weaknesses. In this research, to address the weaknesses of each metaheuristic algorithm, a combination of genetic, ant colony and whale optimization algorithms, is used as the wrapper feature selection. Obviously, the application of early software fault prediction methods before the actual test is one of the effective passive defense techniques in reducing the software system development costs. 19 software projects are used to evaluate the proposed method. Comparison of the results with other methods shows that the proposed method outperforms the counterparts.
Keywords
Software Fault Prediction; Feature Selection; Metaheuristic Algorithm; Genetic Algorithm; Whale Optimization Algorithm; Ant Colony Optimization Algorithm
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