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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Abdul R., Aleisa E. and Bakhsh I. (2013). GUI Test Coverage Analysis using NSGA II, The Proceeding Of International Conference on Soft Computing and Software Engineering [SCSE’13], San Francisco State University, CA, U. S. A., March 2013.
[2]	Pfleeger S. L. (2001). Software Engineering Theory and Practice, Prentice Hall.
[3]	Chayanika S., Sangeeta S. and Ritu S. (2013). A Survey on Software Testing Techniques using Genetic Algorithm, International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013.
[4]	Glenford J. M. (2004). The Art of software Testing, Second Edition, Revised and Updated by Tom Badgett and Todd M. Thomas with Corey Sandler, John Wiley & Sons, Inc. 2004.
[5]	Pierre B. and Richard E. F. (2014). Guide to the Software Engineering Body of Knowledge V3.0, A project of IEEE computer Society 2014.
[6]	Muhammad S., Suhaimi I. and Mohd N. M. (2011). A Study on Test Coverage in Software Testing, International conference on Telecommunication Technology and Applications, Proc of CSIT Vol. 5 IACSIT Press, Singapore, 2011.
[7]	Michalewics Z. (1992). Genetic Algorithm + Data Structures = Evolution Programs, Springer, 1992.
[8]	Samarah Amer (2006). Automated Coverage Directed Test Generation Using a Cell-Based Genetic Algorithm (An Unpublished publication).
[9]	Memon A. M. (2001). A Comprehensive Framework for Testing Graphical User Interfaces, Ph. D. Thesis, University of Pittsburgh, Pittsburg, PA.
[10]	Memon A. M. and Soffa M. (2003). Regression Testing of GUI’s, Proceeding of European Software Engineering Conference /FSE’03. Sep. 2003.
[11]	Misurda J., Clause J. A., et al. (2005). Demand-driven structural testing with Dynamic instrumentation, In Proceedings of 27^th International Conference on Software Engineering, 2005 ICSE 2005, pp. 165-175.
[12]	Matteo B, Cyrille C., Tristan G., Jerome G., Thomas Q. and Olivier H. et al., (2009). Covertures: an Innovative Open Framework for code coverage analysis of safety critical applications, Covertures Open Repository at Open-DO.org, http://forge.open-do.org/projects/couverture.
[13]	Sakamoto K., Washizaki H., et al. (2010). Open Code Coverage Framework: A Consistent and Flexible Framework for Measuring Test Coverage Supporting Multiple Programming Languages, 10^th International Conference on Quality Software, QSIC, 2010, pp. 262-269.
[14]	Abdul R., Arfan, J. and Arshad A. S (2010). Fully Automated GUI test coverage analysis using GA, Seventh International conference on information technology. IEEE 2010, pp. 1057-1063.
[15]	Wen-Yang L., Wen-Yuan L. and Tzung-Pei H (2003). Adapting Crossover and Mutation in Genetic Algorithms” Journal of Information Science and Engineering, 19, 889-903 2003.
[16]	Benjamin D., Edda H. and Christian K. (2008). Crossover Can Provably be Useful in Evolutionary Computation.
[17]	Wu, Y., Ji P. and Wang T. (2008). An empirical study of a pure genetic algorithm to solve the capacitated vehicle routing problem, ICIC Express Letters, Vol. 2, No. 1, pp. 41-45, 2008.
[18]	Jones B. F., Sthamer H. H. and D. E. Eyres (1996). Automatic structural testing using Genetic algorithms, The Software Engineering Journal, Vol. 11, No. 5, pp. 299-306, 1996.
[19]	Jones, B. F., Eyres D. E. and Sthamer H. H., (1998). A Strategy for using genetic algorithms to automate branch and fault-based testing, The Computer Journal, Vol. 41, No. 2, pp. 98-107, 1998.
[20]	Michael C. C., McGraw G. and Schatz M. A. (2001). Generating software test data by evolution, IEEE Transactions on Software Engineering, Vol. 27, No. 12, pp.1 085-1110.
[21]	Pargas R., Harrold M. J. and Peck R. (2008). Test-data generation using genetic algorithms, Journal of Software Testing, Verification and reliability, Science and Software Engineering, Vol. 9, No. 4.
[22]	Shunkun Y., Tianlong M. and Jiaqi X. (2014). Improved Ant Algorithms for Software Testing Cases Generation, the Scientific World Journal Vol. 2014, Article ID 392309, 9 pages Hindawi Publishing Corporation.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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