Abstract
Search-based testing supports developers by automatically generating test suites with high coverage, but the effectiveness of a search-based test generator depends on numerous parameters. It is unreasonable to expect developers to understand search algorithms well enough to find the optimal parameter settings for a problem at hand, and even if they did, a static value for a parameter can be suboptimal at any given point during the search. To counter this problem, parameter control methods have been devised to automatically determine and adapt parameter values throughout the search. To investigate whether parameter control methods can also improve search-based generation of test suites, we have implemented and evaluated different methods to control the crossover and mutation rate in the EvoSuite unit test generation tool. Evaluation on a selection of open source Java classes reveals that while parameter control improves the values of mutation and crossover rate successfully during runtime, the positive effects of this improvement are often countered by increased costs of fitness evaluation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arcuri, A., Fraser, G.: Parameter tuning or default values? An empirical investigation in search-based software engineering. Empir. Softw. Eng. 18(3), 594–623 (2013). http://dx.doi.org/10.1007/s10664-013-9249-9
Arcuri, A., Fraser, G.: On the effectiveness of whole test suite generation. In: Le Goues, C., Yoo, S. (eds.) SSBSE 2014. LNCS, vol. 8636, pp. 1–15. Springer, Heidelberg (2014)
Baresi, L., Lanzi, P.L., Miraz, M.: Testful: an evolutionary test approach for java. In: IEEE International Conference on Software Testing, Verification and Validation (ICST), pp. 185–194 (2010)
Bäck, T., Schütz, M.: Intelligent mutation rate control in canonical genetic algorithms. In: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 158–167. Springer, Heidelberg (1996). http://dx.doi.org/10.1007/3-540-61286-6_141
Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)
Eiben, A., Smit, S.: Evolutionary algorithm parameters and methods to tune them. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 15–36. Springer, Berlin Heidelberg (2012)
Fraser, G., Arcuri, A.: EvoSuite: automatic test suite generation for object-oriented software. In: ACM Symposium on the Foundations of Software Engineering (FSE), pp. 416–419 (2011)
Fraser, G., Arcuri, A.: Handling test length bloat. Softw. Test. Verif. Reliab. 23(7), 553–582 (2013)
Fraser, G., Arcuri, A.: Whole test suite generation. IEEE Trans. Softw. Eng. (TSE) 39(2), 276–291 (2013)
Korel, B.: Automated software test data generation. IEEE Trans. Softw. Eng. 16, 870–879 (1990)
Lin, W.Y., Lee, W.Y., Hong, T.P.: Adapting crossover and mutation rates in genetic algorithms. J. Inf. Sci. Eng. 19(5), 889–903 (2003)
McMinn, P.: Search-based software test data generation: a survey: research articles. Softw. Test. Verif. Reliab. 14(2), 105–156 (2004)
Stoean, C., Stoean, R.: Support Vector Machines and Evolutionary Algorithms for Classification. Springer, Switzerland (2014)
The Apache Software Foundation: Apache commons released components (2015). http://commons.apache.org/components.html
Tonella, P.: Evolutionary testing of classes. In: ACM International Symposium on Software Testing and Analysis (ISSTA), pp. 119–128 (2004)
Acknowledgments
This work was supported by the EPSRC project “EXOGEN” (EP/K030353/1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Paterson, D., Turner, J., White, T., Fraser, G. (2015). Parameter Control in Search-Based Generation of Unit Test Suites. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-22183-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22182-3
Online ISBN: 978-3-319-22183-0
eBook Packages: Computer ScienceComputer Science (R0)