Abstract
Search-based software testing is a powerful automated technique to generate test inputs for software. Its goal is to reach a branch or a statement in a program under test. One major limitation of this approach is an insufficiently informed fitness function to guide search toward a test target within nested predicates (constraints). To address this problem we propose fitness functions based on concepts well known to the constraint programming community, such as constrainedness and arity, to rank test candidates. Preliminary experiments promise efficiency and effectiveness for the new fitness functions.
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References
Arcuri, A.: It does matter how you normalise the branch distance in search based software testing. In: ICST, pp. 205–214. IEEE Computer Society (2010)
Baars, A.I., Harman, M., Hassoun, Y., Lakhotia, K., McMinn, P., Tonella, P., Vos, T.E.J.: Symbolic search-based testing. In: Alexander, P., Pasareanu, C.S., Hosking, J.G. (eds.) ASE, pp. 53–62. IEEE (2011)
Collavizza, H., Rueher, M., Hentenryck, P.V.: Cpbpv: a constraint-programming framework for bounded program verification. Constraints 15, 238–264 (2010)
Gotlieb, A.: Euclide: A constraint-based testing framework for critical C programs. In: ICST, pp. 151–160. IEEE Computer Society (2009)
Lukasiewycz, M., Glaß, M., Reimann, F., Teich, J.: Opt4j: a modular framework for meta-heuristic optimization. In: Krasnogor, N., Lanzi, P.L. (eds.) GECCO, pp. 1723–1730. ACM (2011)
McMinn, P.: Search-based software test data generation: a survey. Software Testing Verification & Reliability 14, 105–156 (2004)
Pasareanu, C.S., Rungta, N.: Symbolic pathfinder: symbolic execution of java bytecode. In: Pecheur, C., Andrews, J., Nitto, E.D. (eds.) ASE, pp. 179–180. ACM (2010)
Pesant, G.: Counting solutions of CSPS: A structural approach. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI, pp. 260–265. Professional Book Center (2005)
Sakti, A., Guéhéneuc, Y.G., Pesant, G.: Boosting search based testing by using constraint based testing. In: Fraser, G., de Souza, J.T. (eds.) SSBSE 2012. LNCS, vol. 7515, pp. 213–227. Springer, Heidelberg (2012)
Tracey, N., Clark, J.A., Mander, K., McDermid, J.A.: An automated framework for structural test-data generation. In: ASE, pp. 285–288 (1998)
Wegener, J., Baresel, A., Sthamer, H.: Evolutionary test environment for automatic structural testing. Information and Software Technology 43(14), 841–854 (2001)
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Sakti, A., Guéhéneuc, YG., Pesant, G. (2013). Constraint-Based Fitness Function for Search-Based Software Testing. In: Gomes, C., Sellmann, M. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2013. Lecture Notes in Computer Science, vol 7874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38171-3_29
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DOI: https://doi.org/10.1007/978-3-642-38171-3_29
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