Open Access

ARTICLE

Generating of Test Data by Harmony Search Against Genetic Algorithms

by Ahmed S. Ghiduk^1,2,*, Abdullah Alharbi¹

1 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
2 Department of Mathematics and Computer Science, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt

* Corresponding Author: Ahmed S. Ghiduk. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 647-665. https://doi.org/10.32604/iasc.2023.031865

Received 28 April 2022; Accepted 29 June 2022; Issue published 29 September 2022

Abstract

Many search-based algorithms have been successfully applied in several software engineering activities. Genetic algorithms (GAs) are the most used in the scientific domains by scholars to solve software testing problems. They imitate the theory of natural selection and evolution. The harmony search algorithm (HSA) is one of the most recent search algorithms in the last years. It imitates the behavior of a musician to find the best harmony. Scholars have estimated the similarities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains. The test data generation process represents a critical task in software validation. Unfortunately, there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process. This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed of finding the required test data. The current research performs an empirical comparison of the HSA and the GAs, and then the significance of the results is estimated using the t-Test. The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to (1) the time performance, (2) the significance of the generated test data, and (3) the adequacy of the generated test data to satisfy a given testing criterion. The results showed that the harmony search algorithm is significantly faster than the genetic algorithms because the t-Test showed that the p-value of the time values is 0.026 < α (α is the significance level = 0.05 at 95% confidence level). In contrast, there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of the fitness values is 0.25 > α.

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Keywords

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