Abstract
Search-Based Testing (SBT) uses an evolutionary algorithm to generate test cases. Traditionally, a random selection is used to generate an initial population and also, less often, during the evolution process. Such selection is likely to achieve lower coverage than a guided selection. We define two novel concepts: (1) a constrained population generator (CPG) that generates a diversified initial population that satisfies some test target constraints; and (2) a constrained evolution operator (CEO) that evolves test candidates according to some constraints of the test target. Either the CPG or CEO may substantially increase the chance of reaching adequate coverage with less effort. In this paper, we propose an approach that models a relaxed version of the unit under test as a constraint satisfaction problem. Based on this model and the test target, a CPG generates an initial population. Then, an evolutionary algorithm uses a CEO and this population to generate test input leading to the test target being covered. Our approach combines constraint-based testing (CBT) and SBT and overcomes the limitations associated with each of them. Using eToc, an open-source SBT tool, we implement a prototype of this approach. We present the empirical results of applying both CPG or CEO on three open-source programs and show that CPG or CEO improve SBT performance in terms of branch coverage by 11% while reducing computation time.
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
Preview
Unable to display preview. Download preview PDF.
References
Baars, A., Harman, M., Hassoun, Y., Lakhotia, K., McMinn, P., Tonella, P., Vos, T.: Symbolic search-based testing. In: 26th IEEE/ACM International Conference on ASE, pp. 53 –62 (November 2011)
Clarke, L.: A system to generate test data and symbolically execute programs. IEEE Transactions on Software Engineering, SE 2(3), 215–222 (1976)
Collavizza, H., Rueher, M., Hentenryck, P.V.: Cpbpv: a constraint-programming framework for bounded program verification. Constraints 15, 238–264 (2010)
Godefroid, P., Klarlund, N., Sen, K.: Dart: directed automated random testing. SIGPLAN Not. 40, 213–223 (2005)
Gotlieb, A.: Euclide: A constraint-based testing framework for critical c programs. In: ICST, pp. 151–160 (2009)
Ince, D.C.: The automatic generation of test data. The Computer Journal 30(1), 63–69 (1987)
Inkumsah, K., Xie, T.: Evacon: A framework for integrating evolutionary and concolic testing for object-oriented programs. In: Proc. 22nd IEEE/ACM ASE, pp. 425–428 (November 2007)
Khichane, M., Albert, P., Solnon, C.: Strong Combination of Ant Colony Optimization with Constraint Programming Optimization. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 232–245. Springer, Heidelberg (2010)
Lakhotia, K., Tillmann, N., Harman, M., de Halleux, J.: FloPSy - Search-Based Floating Point Constraint Solving for Symbolic Execution. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 142–157. Springer, Heidelberg (2010)
Malburg, J., Fraser, G.: Combining search-based and constraint-based testing. In: Proceedings of the 2011 International Symposium on Software Testing and Analysis, ISSTA 2011. ACM, New York (2011)
McMinn, P.: Search-based software test data generation: a survey. Software Testing Verification & Reliability 14, 105–156 (2004)
Miller, W., Spooner, D.: Automatic generation of floating-point test data. IEEE Transactions on Software Engineering, SE 2(3), 223–226 (1976)
Myers, G.J.: The art of software testing. John Wiley and Sons (1979)
Păsăreanu, C.S., Rungta, N.: Symbolic pathfinder: symbolic execution of java bytecode. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, ASE 2010, pp. 179–180. ACM, New York (2010)
Sakti, A., Guéhéneuc, Y.G., Pesant, G.: Cp-sst: approche basée sur la programmation par contraintes pour le test structurel du logiciel. In: Septitièmes Journées Francophones de Programmation par Contraintes (JFPC), pp. 289–298 (June 2011)
Sen, K., Agha, G.: CUTE and jCUTE: Concolic Unit Testing and Explicit Path Model-Checking Tools. In: Ball, T., Jones, R.B. (eds.) CAV 2006. LNCS, vol. 4144, pp. 419–423. Springer, Heidelberg (2006)
Sen, K., Marinov, D., Agha, G.: Cute: a concolic unit testing engine for c. SIGSOFT Softw. Eng. Notes 30, 263–272 (2005)
Staats, M., Pǎsǎreanu, C.: Parallel symbolic execution for structural test generation. In: Proceedings of the 19th international Symposium on Software Testing and Analysis, ISSTA 2010, pp. 183–194. ACM, New York (2010)
Tillmann, N., de Halleux, J.: Pex–White Box Test Generation for.NET. In: Beckert, B., Hähnle, R. (eds.) TAP 2008. LNCS, vol. 4966, pp. 134–153. Springer, Heidelberg (2008)
Tonella, P.: Evolutionary testing of classes. SIGSOFT Softw. Eng. Notes 29(4), 119–128 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sakti, A., Guéhéneuc, YG., Pesant, G. (2012). Boosting Search Based Testing by Using Constraint Based Testing. In: Fraser, G., Teixeira de Souza, J. (eds) Search Based Software Engineering. SSBSE 2012. Lecture Notes in Computer Science, vol 7515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33119-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-33119-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33118-3
Online ISBN: 978-3-642-33119-0
eBook Packages: Computer ScienceComputer Science (R0)