Abstract
Verification of the source code of a probabilistic system by means of an explicit-state model-checker is challenging. In most cases, the probabilistic model-checker will run out of memory due to the infamous state space explosion problem. As far as we know, we are the first to introduce the notion of a progress measure for such a model-checker. The progress measure returns a number in the interval [0, 1]. This number captures the amount of progress the model-checker has made towards verifying a particular linear-time property. The larger the number, the more progress the model-checker has made. We prove that the progress measure provides a lower bound of the measure of the set of execution paths that satisfy the property. We also show how to compute the progress measure for checking a particular class of linear-time properties, namely invariants.
This research is supported by NSERC.
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References
Baier, C., Katoen, J.P.: Principles of Model Checking. The MIT Press, Cambridge (2008)
Billingsley, P.: Probability and Measure. John Wiley & Sons, Chichester (1995)
Katoen, J.P., Zapreev, I.S., Hahn, E.M., Hermanns, H., Jansen, D.N.: The ins and outs of the probabilistic model checker MRMC. Performance Evaluation 68(2), 90–104 (2011)
Kemeny, J.G., Snell, J.L., Knapp, A.W.: Denumerable Markov Chains. Van Nostrand, New York (1966)
Kwiatkowska, M., Norman, G., Parker, D.: Probabilistic symbolic model checking with PRISM: A hybrid approach. International Journal on Software Tools for Technology Transfer 6(2), 128–142 (2004)
Pavese, E., Braberman, V., Uchitel, S.: My model checker died!: how well did it do? In: Proceedings of the 2010 ICSE Workshop on Quantitative Stochastic Models in the Verification and Design of Software Systems, pp. 33–40. ACM, New York (2010)
Della Penna, G., Intrigila, B., Melatti, I., Tronci, E., Venturini Zilli, M.: Finite horizon analysis of Markov chains with the Murϕ verifier. International Journal on Software Tools for Technology 8(4/5), 397–409 (2006)
Vardi, M.Y.: Automatic verification of probabilistic finite-state programs. In: Proceedings of the 26th IEEE Symposium on Foundations of Computer Science, pp. 327–338. IEEE, Los Alamitos (1985)
Visser, W., Havelund, K., Brat, G., Park, S., Lerda, F.: Model checking programs. Automated Software Engineering 10(2), 203–232 (2003)
Zhang, X.: Measuring Progress of Model Checking Randomized Algorithms. Master’s thesis. York University, Toronto (2010)
Zhang, X., Breugel, F.v.: Measuring progress of Java PathFinder model-checking randomized sequential code. In: Preliminary Proceedings of the 6th Workshop on Quantitative Aspects of Programming Languages, 4 pages (2008)
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Zhang, X., van Breugel, F. (2011). A Progress Measure for Explicit-State Probabilistic Model-Checkers. In: Aceto, L., Henzinger, M., Sgall, J. (eds) Automata, Languages and Programming. ICALP 2011. Lecture Notes in Computer Science, vol 6756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22012-8_22
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DOI: https://doi.org/10.1007/978-3-642-22012-8_22
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