An SMT-Based Concolic Testing Tool for Logic Programs

[EN] Concolic testing combines symbolic and concrete execution to generate test cases that achieve a good program coverage. Its benefits have been demonstrated for more than 15 years in the case of imperative programs. In this work, we present a concolic-based test generation tool for logic programs which exploits SMT-solving for constraint resolutionThird author is a research associate at FNRS that also supports this work (O05518FRG03). The last author is partially supported by the EU (FEDER) and the Spanish MCI/AEI under grants TIN2016-76843-C4-1-R/PID2019-104735RB-C41 and by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust)Fortz, S.; Mesnard, F.; Payet, E.; Perrouin, G.; Vanhoof, W.; Vidal, G. (2020). An SMT-Based Concolic Testing Tool for Logic Programs. Springer Nature. 215-219. https://doi.org/10.1007/978-3-030-59025-3_13S215219de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24Giantsios, A., Papaspyrou, N., Sagonas, K.: Concolic testing for functional languages. Sci. Comput. Program. 147, 109–134 (2017)Godefroid, P., Klarlund, N., Sen, K.: DART: directed automated random testing. In: Proceedings of PLDI 2005, pp. 213–223. ACM (2005)Mesnard, F., Payet, É., Vidal, G.: Concolic testing in logic programming. TPLP 15(4–5), 711–725 (2015). https://doi.org/10.1017/S1471068415000332Mesnard, F., Payet, É., Vidal, G.: On the completeness of selective unification in concolic testing of logic programs. In: Hermenegildo, M.V., Lopez-Garcia, P. (eds.) LOPSTR 2016. LNCS, vol. 10184, pp. 205–221. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63139-4_12Mesnard, F., Payet, É., Vidal, G.: Selective unification in constraint logic programming. In: Vanhoof, W., Pientka, B. (eds.) PPDP, pp. 115–126. ACM (2017)Mesnard, F., Payet, É., Vidal, G.: Concolic Testing in CLP. CoRR abs/2008.00421 (2020). https://arxiv.org/abs/2008.00421Sen, K., Marinov, D., Agha, G.: CUTE: a concolic unit testing engine for C. In: ESEC/ FSE, pp. 263–272. ACM (2005)Ströder, T., Emmes, F., Schneider-Kamp, P., Giesl, J., Fuhs, C.: A linear operational semantics for termination and complexity analysis of ISO Prolog. In: Vidal, G. (ed.) LOPSTR 2011. LNCS, vol. 7225, pp. 237–252. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32211-2_16Tikovsky, J.R.: Concolic testing of functional logic programs. In: Seipel, D., Hanus, M., Abreu, S. (eds.) WFLP/WLP/INAP -2017. LNCS (LNAI), vol. 10997, pp. 169–186. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00801-7_11Vidal, G.: Concolic execution and test case generation in prolog. In: Proietti, M., Seki, H. (eds.) LOPSTR 2014. LNCS, vol. 8981, pp. 167–181. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17822-6_10Wielemaker, J., Schrijvers, T., Triska, M., Lager, T.: SWI-prolog. TPLP 12(1–2), 67–96 (2012). https://doi.org/10.1017/S147106841100049

Integration of Finite Domain Constraints in KiCS2 ∗

Abstract: A constraint programming system usually consists of two main components: a modelling language used to describe a constraint satisfaction problem and a constraint solver searching for solutions to the given problem by applying specific algorithms. As constraint programming and functional logic languages share some common features, like computing with logic variables or the use of backtracking for non-deterministic search, it is reasonable to embed a modelling language for finite domain constraints in a functional logic language like Curry. Due to the absence of side effects or global state over non-deterministic computations in Curry, the implementation of a stateful constraint solver is rather difficult. In this paper we consider KiCS2, a Curry compiler translating Curry programs into Haskell programs. In order to embed finite domain constraints in KiCS2, we propose a new implementation technique compatible with the purely functional nature of its back end. Our implementation collects finite domain constraints occurring during a program run and passes them to a constraint solver available in Haskell whenever solutions are requested.