Application of ACO in Model Based Software Testing: A Review
DOI:
https://doi.org/10.26438/ijcse/v6i3.370374Keywords:
Ant Colony, Optimization, Model Based Software Testing, Optimal Path, State MachineAbstract
Software Testing is the process of testing the software in order to ensure that it is free of errors and produces the desired outputs in any given situation. Properly generated test suites may not only locate the defects in software systems, but also help in reducing the high cost associated with software testing. Model based software testing is an approach in which software is viewed as a set of states. There are a number of models of software in use today, a few of which make good models for testing. This paper introduces model-based testing and discusses its tasks in general terms with finite state models. Ant colony optimization (ACO) is best suited to model based software testing like finite state machines, state charts, the unified modeling language (UML) and Markov chains.
References
Huaizhong LI, C. Peng LAM, “An Ant Colony Optimization Approach To Test Sequence Generation For State Based Software Testing”, Fifth International Conference on Quality Software (QSIC’05), pp. 255-264, 2005.
Dan Liu,Xuejun Wang, Jianmin Wang, “Automatic Test Case Generation Based On Genetic Algorithm”, Journal of Theoretical and Applied Information Technology, Vol. 48 No. 1, pp. 411-416, 2013.
Praveen Ranjan Srivastava1, Nitin Jose, Saudagar Barade, Debopriyo Ghosh, “Optimized Test Sequence Generation From Usage Models Using Ant Colony Optimization”, IJSEA, Vol.1, No.2, pp. 14-28, 2010.
Navneet Kaur, Jaspreet Singh Budwal, “Hybrid Approach to Retrieval of Reusable Component from a Repository Using Genetic Algorithms and Ant Colony”, International Conference on Genetic and Evolutionary Method, Las Vegas, Nevada, USA , pp.147-152, 2008.
Rafael S. Parpinelli1, Heitor S. Lopes1, And Alex A. Freitas2, “Data Mining With An Ant Colony Optimization Algorithm”, IEEE Transactions on Evolutionary Computation”, Vol. 6, Issue: 4, pp. 321 – 332, 2002.
Praveen Ranjan Srivastava1 and Tai-hoon Kim, “Application of Genetic Algorithm in Software Testing”, International Journal of Software Engineering and Its Applications, Vol. 3, No.4, pp. 87-96, October 2009.
Navneet Kaur, Jaspreet Singh Budwal ,“Intelligent Web Search Optimization with reference to Mutation Operator of Genetic and Cultural Algorithms Framework”, 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT), pp. 619-623, 2014.
Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi, “Ant Colony Optimization”, Studies in Fuzziness and Soft Computing book series STUDFUZZ, Vol 141, pp 101-121.
Dorigo, M., Di Caro, G. & Gambardella, L. M. “Ant algorithms for
discrete optimization”. Artificial Life Vol 5, No. 2, 137-172, 1999.
Huaizhong Li and C. Peng Lam, “Software Test Data Generation using Ant Colony Optimization”, International Journal of Computer, Information Science and Engineering Vol:1 No:1, pp 126-129, 2007.
M. Dorigo, A. Colorni and V. Maniezzo, “The Ant System: optimization
by a colony of cooperating agents,” IEEE Transactions on Systems,
Man, and Cybernetics-Part B, vol. 26, No. 1, pp. 29-41, 1996.
L.M Gambardella and M. Dorigo M, “Solving Symmetric and Asymmetric TSPs by Ant Colonies”, Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, Nagoya, Japan, May 20-22, pp. 622-627, 1996.
Ahmed S. Ghiduk, “A New Software Data-Flow Testing Approach via
Ant Colony Algorithms”, Universal Journal of Computer Science and
Engineering Technology, pp 64-72, 2010.
Neha Pahwa, Kamna Solanki, “UML based Test Case Generation
Methods: A Review”, International Journal of Computer Applications,
Vol 95– No.20, pp 1-6, 2014.
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