Software Test Data Generation using Ant Colony Optimization

Authors: Huaizhong Li, C.Peng Lam

Abstract:

State-based testing is frequently used in software testing. Test data generation is one of the key issues in software testing. A properly generated test suite may not only locate the errors in a software system, but also help in reducing the high cost associated with software testing. It is often desired that test data in the form of test sequences within a test suite can be automatically generated to achieve required test coverage. This paper proposes an Ant Colony Optimization approach to test data generation for the state-based software testing.

Keywords: Software testing, ant colony optimization, UML.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330789

References:

[1] Binder, R. V., Testing Object-oriented Systems: Models, Patterns, and Tools, Addison-Wesley. 2000.
[2] Briand, L. C.,''On the many ways Software Engineering can benefit from Knowledge Engineering'', Proc. 14th SEKE, Italy, pp. 3-6, 2002.
[3] Doerner, K., Gutjahr, W. J., ''Extracting Test Sequences from a Markov Software Usage Model by ACO'', LNCS, Vol. 2724, pp. 2465-2476, Springer Verlag, 2003.
[4] Dorigo M., Maniezzo, V., Colorni, A., ''Positive Feedback as a Search Strategy'', Technical Report No. 91-016, Politecnico di Milano, Italy, 1991.
[5] Dorigo M., Maniezzo, V., Colorni, A., ''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.
[6] Horgan, J., London, S., and Lyu, M., ''Achieving Software Quality with Testing Coverage Measures'', IEEE Computer, Vol. 27 No.9 pp. 60-69, 1994.
[7] Howe, A. E., Mayrhauser A. V., and Mraz, R. T., ''Test Case Generation as an AI Planning Problem'', Automated Software Engineering, Vol. 4, pp 77-106, 1997.
[8] Li, H., Lam, C.P., ''Optimization of State-based Test Suites for Software Systems: An Evolutionary Approach'', International Journal of Computer & Information Science, Vol. 5, No. 3, pp. 212-223, 2004.
[9] McMinn, P., ''Search-based Software Test Data Generation: A Survey'', Software Testing, Verification and Reliability, Vol.14, No. 2, pp. 105-156, 2004.
[10] McMinn, P., Holcombe, M., ''The State Problem for Evolutionary Testing'', Proc. GECCO 2003, LNCS Vol. 2724, pp. 2488-2500, Springer Verlag, 2003.
[11] Pargas, R. P., Harrold, M. J., and Peck, R., ''Test-Data Generation Using Genetic Algorithms'', Software Testing, Verification and Reliability, Vol. 9, pp. 263 - 282, 1999.
[12] Pedrycz, W., Peters, J. F., Computational Intelligence in Software Engineering, World Scientific Publishers, 1998.
[13] Tracey, N., Clark, N., .Mander K., and McDermid, N., ''A Search Based Automated Test Data Generation Framework for Safety Critical Systems'', in Systems Engineering for Business Process Change (New Directions), Henderson P., Editor, Springer Verlag, 2002.
[14] Wagner, I. A., Lindenbaum, M., Bruckstein, A. M., ''ANTS: Agents, Networks, Trees, and Subgraphs'', Special issue on Ant Colony Optimization (M. Dorigo, G. Di Caro, T.St├╝tzle (eds)), Future Generation Computer Systems, Vol. 16, No. 8, pp. 915-926, North Holland, June 2000.