An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software

Asade Mojeed Adeniyi; Akinola Solomon Olalekan

doi:doi:10.11648/j.ajsea.20160502.11

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An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software

Asade Mojeed Adeniyi, Akinola Solomon Olalekan

Published in American Journal of Software Engineering and Applications (Volume 5, Issue 2)

Received: 22 January 2016 Accepted: 3 February 2016 Published: 6 April 2016

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Abstract

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.

Published in	American Journal of Software Engineering and Applications (Volume 5, Issue 2)
DOI	10.11648/j.ajsea.20160502.11
Page(s)	7-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Software Test Coverage Analysis, Graphical User Interface, Quality Software, Genetic Algorithm

References

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APA Style

Asade Mojeed Adeniyi, Akinola Solomon Olalekan. (2016). An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. American Journal of Software Engineering and Applications, 5(2), 7-14. https://doi.org/10.11648/j.ajsea.20160502.11

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ACS Style

Asade Mojeed Adeniyi; Akinola Solomon Olalekan. An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. Am. J. Softw. Eng. Appl. 2016, 5(2), 7-14. doi: 10.11648/j.ajsea.20160502.11

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AMA Style

Asade Mojeed Adeniyi, Akinola Solomon Olalekan. An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. Am J Softw Eng Appl. 2016;5(2):7-14. doi: 10.11648/j.ajsea.20160502.11

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@article{10.11648/j.ajsea.20160502.11,
  author = {Asade Mojeed Adeniyi and Akinola Solomon Olalekan},
  title = {An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {2},
  pages = {7-14},
  doi = {10.11648/j.ajsea.20160502.11},
  url = {https://doi.org/10.11648/j.ajsea.20160502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20160502.11},
  abstract = {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.},
 year = {2016}
}

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TY  - JOUR
T1  - An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software
AU  - Asade Mojeed Adeniyi
AU  - Akinola Solomon Olalekan
Y1  - 2016/04/06
PY  - 2016
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DO  - 10.11648/j.ajsea.20160502.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
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PB  - Science Publishing Group
SN  - 2327-249X
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AB  - 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.
VL  - 5
IS  - 2
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Author Information

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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APA Style

Asade Mojeed Adeniyi, Akinola Solomon Olalekan. (2016). An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. American Journal of Software Engineering and Applications, 5(2), 7-14. https://doi.org/10.11648/j.ajsea.20160502.11

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ACS Style

Asade Mojeed Adeniyi; Akinola Solomon Olalekan. An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. Am. J. Softw. Eng. Appl. 2016, 5(2), 7-14. doi: 10.11648/j.ajsea.20160502.11

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AMA Style

Asade Mojeed Adeniyi, Akinola Solomon Olalekan. An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software. Am J Softw Eng Appl. 2016;5(2):7-14. doi: 10.11648/j.ajsea.20160502.11

Copy | Download

@article{10.11648/j.ajsea.20160502.11,
  author = {Asade Mojeed Adeniyi and Akinola Solomon Olalekan},
  title = {An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {2},
  pages = {7-14},
  doi = {10.11648/j.ajsea.20160502.11},
  url = {https://doi.org/10.11648/j.ajsea.20160502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20160502.11},
  abstract = {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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - An Improved Genetic Algorithm-Based Test Coverage Analysis for Graphical User Interface Software
AU  - Asade Mojeed Adeniyi
AU  - Akinola Solomon Olalekan
Y1  - 2016/04/06
PY  - 2016
N1  - https://doi.org/10.11648/j.ajsea.20160502.11
DO  - 10.11648/j.ajsea.20160502.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 7
EP  - 14
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20160502.11
AB  - 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.
VL  - 5
IS  - 2
ER  -

Copy | Download