Genetic algorithms have been successfully applied in the area of software testing. The demand for automation of test case generation in object oriented software testing is increasing. Genetic algorithms are well applied in procedural software testing but a little has been done in testing of object oriented software. This paper discusses genetic algorithms that can automatically select an efficient algorithm which is suitable for test cases selection. This algorithm takes a selected path as a target and executes sequences of operators iteratively for efficient algorithm selection to evolve. The evolved efficient algorithm selection can lead the program execution to achieve the target path. An automatic path-oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. We also propose genetic algorithm for the selection of the suitable algorithm, which perform much better than the existing methods and can provide very good solutions.

1. Alspaughy,S.,Walcotty,K.R.,Belanichz,M.,Kapfhammerz,G.M.,and Soffa,M.L.," Efficient Time-Aware Prioritization with Knapsack Solvers", Proceedings of the ASE 2007 Workshop on Empirical Assessment of Software Engineering Languages and Technologies. Atlanta, Georgia, pp. 1-6.
2. Askarunisa, A., Shanmugapriya, L., Ramaraj. N., "Cost and Coverage Metrics for Measuring the Effectiveness of Test Case Prioritization Techniques", INFOCOMP Journal of Computer Science, pp. 1-10.
3. Shin Yoo and Mark Harman,"Using hybrid algorithm for Pareto effcient multi objective test suite minimization", Journal of Systems Software, 83(4):689–701, April 2010.
4. Mark Harman," Making the case for morto: Multi objective regression test optimization", In The 1st International Workshop on Regression Testing (Regression 2011), Berlin, Germany, 2011.
5. Antonia, B., "Software Testing Research: Achievements, Challenges, Dreams", in 2007 Future of Software Engineering: IEEE Computer Society, 2007.
6. Christian Borgelt, "An Implementation of the FP-growth Algorithm".
7. Kant Singh,V., Shah,V., Kumar Jain,Y., Shukla,A., Thoke,A.S., Kumar Singh,V., Dule,C., and Parganiha ,V.,"Proposing an Efficient Method for Frequent Pattern Mining".
8. Wegener,J., and Grochtmann,M., "Verifying timing constraints by means of evolutionary testing", Real-Time Systems, Vol.3, No.15, pp. 275-298, 1998.
9. Wappler,S., and Lammermann,F., "Using evolutionary algorithms for the unit testing of object-oriented software", Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, Washington DC, USA, June 25-29, ACM, New York, pp. 1053-1060, 2005.
10. Tonella,P., "Evolutionary Testing of Classes", Proceedings of the 2004 ACM SIGSOFT Intl. Symposium on Software Testing and Analysis, Boston, July 11-14, pp. 119-128, 2004.
11. B Jones et al. "Automatic Structural Testing Using Genetic Algorithms", Software Engineering Journal, Vol.11, No.5, 1996.
12. McMinn,P., "Search-Based Software Test Data Generation: A Survey", Software Testing, Verification and Reliability, Vol.14, No.2, pp. 105—156, 2004.
13. Koza,D., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
14. Seesing,A., and Gross,H.G., "A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software", International Transactions on Systems Science and Applications, Vol.1, No.2, pp.127-134, 2006.
15. Peter M. Kruse and Magdalena Luniak" Automated test case generation using classification trees. ASQ Software Quality Professional, 13:4–12, December 2010.
16. Arturo Hern/andez Aguirre, Salvador Botello Rionda, Carlos A. Coello Coello, Giovanni Liz/arraga Liz/arraga, and Efr/en Mezura Montes," Handling Constraints using Multiobjective Optimization Concepts", International Journal for Numerical Methods in Engineering, 59(15):1989–2017, April 2004.

Conformance Testing Prioritized Test Case Generation Test Case Selection