Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Abstract

Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

Software fault prediction using Whale algorithm with genetics algorithm

“…The average accuracy values show that SVM outperforms the other classifiers and obtains the best performance in all of the dataset, 47 SVM scores the least average of root means a square error, which means it is the most robust classifier among others. Therefore, SVM is chosen as a classifier to be integrated with optimization algorithms.…”

“…This paper is considered as future and extended work of our previous published paper 47 so in order to validate the theoretical developed model, experiments were conducted by using MATLAB 2015 environment and WEKA Tool; the results of the experiments have been evaluated based on evaluation measures to test the robustness and effectiveness of the proposed software fault prediction model.…”

“…In this paper we used the same preliminary step that was conducted in our previous paper, 47 the most commonly used classification techniques for feature selection: NaiveBayes, J48, Decision Tree, and SVM are applied into four common used dataset (CM1, KC1, KC2, PC1), 10‐folds cross‐validation technique is used and the iteration control repeated 10 times for each dataset, finally the output results are extracted into CSV file. The purpose of conducting the preliminary experiments on classification techniques is to choose a suitable classifier for feature selection and combine it with other optimization techniques.…”

Research Trends in Software Engineering Field: A Literature Review

“…In [5], an approach was developed by integrating support vector machine (SVM) with genetics algorithm (GA) and particle swarm algorithm for software fault prediction. The developed approach is applied (12-NASA MDP and 12-Java open-source projects).…”

Harnessing deep learning algorithms to predict software refactoring

“…Different methodologies are designed and built to help developers in the refactoring process such as code smells detection strategies [12], logic meta-programming [13], invariant mining [14] and search-based [15,16]. Moreover, machine learning is harnessed in the area of prediction and shows noticeable performance in terms of prediction in various fields as computer vision, defect prediction, natural language processing, code comprehension, bioinformatics, speech recognition, and finance [17][18][19][20][21][22][23][24]. Several machine learning algorithms are utilized in code refactoring prediction at class and method level as well [25,26].…”