Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
Dynamic generation of test cases with metaheuristics
Keywords:
Software Testing, Evolutionary Testing, Particle Swarm Optimization, Evolutionary Algorithms, MetaheuristicsAbstract
The resolution of optimization problems is of great interest nowadays and has encouraged the development of various information technology methods to attempt solving them. There are several problems related to Software Engineering that can be solved by using this approach. In this paper, a new alternative based on the combination of population metaheuristics with a Tabu List to solve the problem of test cases generation when testing software is presented. This problem is of great importance for the development of software with a high computational cost and which is generally hard to solve. The performance of the solution proposed has been tested on a set of varying complexity programs. The results obtained show that the method proposed allows obtaining a reduced test data set in a suitable timeframe and with a greater coverage than conventional methods such as Random Method or Tabu Search.
Downloads
Download data is not yet available.
References
[1] Bird S., Li X. Adaptively Choosing Niching Parameters in a PSO. Proceeding of Genetic and Evolutionary Computation Conference 2006 (GECCO'06), eds. M. Keijzer, et al., p.3 - 9, ACM Press. 2006.
[2] Bird D., Muñoz C. Automatic generation of random self-checking test cases. IBM Systems Journal, 22(3):229-245, 1983.
[3] Bouchachia A. An Immune Genetic Algorithm for Software Test Data Generation. Seventh International Conference on Hybrid Intelligent Systems. 2007. pp.84-89
[4] Clerc M., Kennedy J. The particle swarm –explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. Vol 6,nro. 1, pp. 58-73. Feb.2002
[5] Díaz E., Tuya J., Blanco R. Automated Software Testing using a Metaheuristic Techmique based on Tabu Search. 18 th IEEE International Conference on Automated Software Engineering. pp.310- 313. ISBN: 0-7695-2035-9. 2003
[6] Kenedy J. and Eberhart R. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. Vol IV, pp.1942-1948. Australia 1995
[7] Lanzarini L., Leza V., De Giusti A. Particle Swarm Optimization with Variable Population Size. Lecture Notes in Computer Science. Vol 5097/2008. Artificial Intelligence and Soft Computing – ICAISC 2008. ISBN 978-3-540-69572-1. pp.438-449. June 2008
[8] Lopez J., Lanzarini L., De Giusti A. Particle Swarm Optimization with Oscillation Control. Genetic and Evolutionary Computation Conference. ACM GEECCO Proceeding. Montréal, Canada. July 2009.
[9] Michael C., McGraw G., and M. A. Schatz. Generating software test data by evolution. IEEE Transactions on Software Engineering, 27(12):1085-1110, 2001.
[10] Nieto J. Algorithms based on swarms of particles for solving complex problems. University Málaga. (In Spanish). 2006.
[11] Offutt J. An integrated automatic test data generation system. Journal of Systems Integration, 1(3):391-409, November 1991.
[12] Pargas R., Harrold M., Peck R. Test-Data generation using Genetic Algorithms. Journal of Software Testing, Verification and Reliability. Vol 9. pp.263-282.1999
[13] Sagarna R., Lozano J. Scatter Search in software testing, comparison and collaboration with Estimation of Distribution Algorithms. European Journal of Operational Research 169 (2006) 392–412
[14] Shi Y., Eberhart R. An empirical study of particle swarm optimization. Proceeding on IEEE Congress Evolutionary Computation. pp.1945-1949. Washington DC, 1999.
[15] Shi Y., Eberhart R. Parameter Selection in Particle Swarm Optimization. Proceedings of the 7th International Conference on Evolutionary Programming. pp. 591-600. Springer Verlag 1998. ISBN 3-540-64891-7
[16] Tracey N., Clark J., Mander K. Automated program flaw finding using simulated annealing. International Symposium on Software Testing and Analysis. 1998. pp. 73-81. ACM/SIGSOFT
[17] Van den Bergh F. An analysis of particle swarm optimizers. Ph.D. dissertation. Department Computer Science. University Pretoria. South Africa. 2002
[2] Bird D., Muñoz C. Automatic generation of random self-checking test cases. IBM Systems Journal, 22(3):229-245, 1983.
[3] Bouchachia A. An Immune Genetic Algorithm for Software Test Data Generation. Seventh International Conference on Hybrid Intelligent Systems. 2007. pp.84-89
[4] Clerc M., Kennedy J. The particle swarm –explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. Vol 6,nro. 1, pp. 58-73. Feb.2002
[5] Díaz E., Tuya J., Blanco R. Automated Software Testing using a Metaheuristic Techmique based on Tabu Search. 18 th IEEE International Conference on Automated Software Engineering. pp.310- 313. ISBN: 0-7695-2035-9. 2003
[6] Kenedy J. and Eberhart R. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. Vol IV, pp.1942-1948. Australia 1995
[7] Lanzarini L., Leza V., De Giusti A. Particle Swarm Optimization with Variable Population Size. Lecture Notes in Computer Science. Vol 5097/2008. Artificial Intelligence and Soft Computing – ICAISC 2008. ISBN 978-3-540-69572-1. pp.438-449. June 2008
[8] Lopez J., Lanzarini L., De Giusti A. Particle Swarm Optimization with Oscillation Control. Genetic and Evolutionary Computation Conference. ACM GEECCO Proceeding. Montréal, Canada. July 2009.
[9] Michael C., McGraw G., and M. A. Schatz. Generating software test data by evolution. IEEE Transactions on Software Engineering, 27(12):1085-1110, 2001.
[10] Nieto J. Algorithms based on swarms of particles for solving complex problems. University Málaga. (In Spanish). 2006.
[11] Offutt J. An integrated automatic test data generation system. Journal of Systems Integration, 1(3):391-409, November 1991.
[12] Pargas R., Harrold M., Peck R. Test-Data generation using Genetic Algorithms. Journal of Software Testing, Verification and Reliability. Vol 9. pp.263-282.1999
[13] Sagarna R., Lozano J. Scatter Search in software testing, comparison and collaboration with Estimation of Distribution Algorithms. European Journal of Operational Research 169 (2006) 392–412
[14] Shi Y., Eberhart R. An empirical study of particle swarm optimization. Proceeding on IEEE Congress Evolutionary Computation. pp.1945-1949. Washington DC, 1999.
[15] Shi Y., Eberhart R. Parameter Selection in Particle Swarm Optimization. Proceedings of the 7th International Conference on Evolutionary Programming. pp. 591-600. Springer Verlag 1998. ISBN 3-540-64891-7
[16] Tracey N., Clark J., Mander K. Automated program flaw finding using simulated annealing. International Symposium on Software Testing and Analysis. 1998. pp. 73-81. ACM/SIGSOFT
[17] Van den Bergh F. An analysis of particle swarm optimizers. Ph.D. dissertation. Department Computer Science. University Pretoria. South Africa. 2002
Downloads
Published
2010-06-01
Issue
Section
Original Articles
License
How to Cite
[1]
“Dynamic generation of test cases with metaheuristics”, JCS&T, vol. 10, no. 02, pp. p. 91–96, Jun. 2010, Accessed: Jan. 17, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/733
