An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software
American Journal of Software Engineering and Applications
Volume 5, Issue 2, April 2016, Pages: 7-14
Received: Jan. 22, 2016; Accepted: Feb. 3, 2016; Published: Apr. 6, 2016
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Asade Mojeed Adeniyi, Department of Computer Science, University of Ibadan, Ibadan, Nigeria
Akinola Solomon Olalekan, Department of Computer Science, University of Ibadan, Ibadan, Nigeria
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Quality and reliability of software products can be determined through the amount of testing that is carried out on them. One of the metrics that are often employed in measuring the amount of testing is the coverage analysis or adequacy ratio. In the proposed optimized basic Genetic Algorithm (GA) approach, a concept of adaptive mutation was introduced into the basic GA in order for low-fitness chromosomes to have an increased probability of mutation, thereby enhancing their role in the search to produce more efficient search. The main purpose of this concept is to decrease the chance of disrupting a high-fitness chromosome and to have the best exploitation of the exploratory role of low-fitness chromosome. The study reveals that the optimized basic GA improves significantly the adequacy ratio or coverage analysis value for Graphical User Interface (GUI) software test over the existing non-adaptive mutation basic GA.
Software Test Coverage Analysis, Graphical User Interface, Quality Software, Genetic Algorithm
To cite this article
Asade Mojeed Adeniyi, Akinola Solomon Olalekan, An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software, American Journal of Software Engineering and Applications. Vol. 5, No. 2, 2016, pp. 7-14. doi: 10.11648/j.ajsea.20160502.11
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