Abstract

UML models have gained their significance as reported in the literature. The use of a model to describe the behavior of a system is a proven and major advantage to test. With the help of Model Based Testing (MBT), it is possible to automatically generate test cases. When MBT is applied on large industrial systems, there is problem to sampling the test cases from the suit of entire test because it is difficult to execute the huge number of test cases being generated. The motivation of this study is to design a multi objective genetic algorithm based test case selection technique which can select the most appropriate subset of test cases. NSGA (Non-dominated Sorting Genetic Algorithm) is used as an optimization algorithm and its fitness function is improved for selecting test cases from the dataset. It is concluded that there is a room to improve the performance of NSGA algorithm by means of tailoring its respective fitness function.

Keywords:

Metaheuristic, MBT, regression testing, test case selection,

References

1. Ali, A., A. Nadeem, Z. Iqbal and M. Usman, 2007. Regression testing based on UML design models. Proceeding of the 13th IEEE International Symposium on Pacific Rim Dependable Computing, pp: 85-88.
   CrossRef    
2. Ali, S., L. Briand, H. Hemmati and R. Panesar-Walawege, 2008. A systematic review of the application and empirical investigation of search-based test case generation. IEEE T. Software Eng., 36(6): 742-762.
   CrossRef    
3. Coello, C.A.C.C., 2001. A Short Tutorial on Evolutionary Multiobjective Optimization. In: Zitzler, E. et al. (Eds.): EMO 2001. LNCS 1993, Springer-Verlag, Berlin, Heidelberg, pp: 21-40.
   CrossRef    
4. Coley, D.A., 1999. An Introduction to Genetic Algorithm for Scientists and Engineers. World Scientific Publishing Co. Pte. Ltd., Singapore.
   CrossRef    
5. Deb, K., A. Pratap, S. Agarwal and T. Meyarivan, 2002. A fast and elitist multi objective genetic algorithm: NSGA II. IEEE T. Evolut. Comput., 6(2): 182-197.
   CrossRef    
6. Dias, A.H.F. and J.A. De Vasconcelos, 2002. Multi objective genetic algorithms applied to solve optimization problems. IEEE T. Magn., 38(2): 1133-1136.
   CrossRef    
7. El-Far, I.K., 2001. Enjoying the perks of model-based testing. Proceeding of the Software Testing, Analysis and Review Conference (STARWEST 2001).
   
8. El-Far, I.K. and J.A. Whittaker, 2001. Model-based software testing. In: Marciniak, J.J. (Ed.), Encyclopedia of Software Engineering. Wiley, Chichester.
   
9. Hemmati, H., L. Briand, A. Arcuri and S. Ali, 2011. An enhanced test case selection approach for model-based testing: An industrial case study. Proceedings of the 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp: 267-276.
   
10. Iqbal, M.Z., A. Arcuri and L. Briand, 2012. Empirical investigation of search algorithms for environment model-based testing of real-time embedded software. Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA 2012), pp: 199-209.
    CrossRef    
11. Ma, X., B. Sheng and C. Ye, 2005. Test-suite Reduction Using Genetic Algorithm. In: Cao, J., W. Nejdl and M. Xu (Eds.): APPT 2005. LNCS 3756, Springer-Verlag, Berlin, Heidelberg, pp: 253-262.
    CrossRef    
12. Sieving, R. and P. Öman, 2010. Pilot project for model based testing using conformiq qtronic. M.A. Thesis, Department of Computer Science and Electrical Engineering, Division of Media Technology, Lulea University of Technology, Arena.
    
13. Srinivas, N. and K. Deb, 1994. Multi objective optimization using non-dominated sorting enetic algorithm. J. Evolut. Comput., 2(3): 221-248.
    CrossRef    
14. Utting, M. and B. Legeard, 2006. Practical Model-based Testing: A Tools Approach. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
    PMCid:PMC2361281    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is 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, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.