Abstract
Improving the scalability of probabilistic model checking (PMC) tools is crucial to the verification of real-world system designs. The Stamina infinite-state PMC tool achieves scalability by iteratively constructing a partial state space for an unbounded continuous-time Markov chain model, where a majority of the probability mass resides. It then performs time-bounded transient PMC. It can efficiently produce an accurate probability bound to the property under verification. We present a new software architecture design and the C++ implementation of the Stamina 2.0 algorithm, integrated with the Storm model checker. This open-source Stamina implementation offers a high degree of modularity and provides significant optimizations to the Stamina 2.0 algorithm. Performance improvements are demonstrated on multiple challenging benchmark examples, including hazard analysis of infinite-state combinational genetic circuits, over the previous Stamina implementation. Additionally, its design allows for future customizations and optimizations to the Stamina algorithm.
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References
Baier, C., Haverkort, B.R., Hermanns, H., Katoen, J.P.: Model-checking algorithms for continuous-time Markov chains. IEEE Trans. Software Eng. 29(6), 524–541 (2003). https://doi.org/10.1109/TSE.2003.1205180
Budde, C.E., et al.: On correctness, precision, and performance in quantitative verification. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020. LNCS, vol. 12479, pp. 216–241. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83723-5_15
Češka, M., Křetínský, J.: Semi-quantitative abstraction and analysis of chemical reaction networks. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 475–496. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25540-4_28
Fontanarrosa, P., Doosthosseini, H., Borujeni, A.E., Dorfan, Y., Voigt, C.A., Myers, C.: Genetic circuit dynamics: hazard and glitch analysis. ACS Synth. Biol. 15 (2020)
Fox, B.L., Glynn, P.W.: Computing poisson probabilities. Commun. ACM 31(4), 440–445 (1988). https://doi.org/10.1145/42404.42409
Hahn, E.M., Hermanns, H., Wachter, B., Zhang, L.: INFAMY: an infinite-state Markov model checker. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 641–647. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02658-4_49
Hensel, C., Junges, S., Katoen, J.P., Quatmann, T., Volk, M.: The probabilistic model checker Storm. Int. J. Softw. Tools Technol. Transfer 24(4), 589–610 (2022). https://doi.org/10.1007/s10009-021-00633-z
Hermanns, H., Meyer-Kayser, J., Siegle, M.: Multi terminal binary decision diagrams to represent and analyse continuous time Markov chains. In: Plateau, B., Stewart, W., Silva, M. (eds.) NSMC, pp. 188–207 (1999)
Jackson, J.: Networks of waiting lines. Oper. Res. 5, 518–521 (1957)
Kwiatkowsa, M., Norman, G., Parker, D.: The PRISM benchmark suite. In: International Conference on Quantitative Evaluation of Systems (QEST), pp. 203–204 (2012). https://doi.org/10.1109/QEST.2012.14. https://doi.ieeecomputersociety.org/10.1109/QEST.2012.14
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22110-1_47
Lapin, M., Mikeev, L., Wolf, V.: Shave: stochastic hybrid analysis of Markov population models. In: Proceedings of the 14th International Conference on Hybrid Systems: Computation and Control, HSCC 2011, pp. 311–312. ACM, New York (2011)
Madsen, C., Zhang, Z., Roehner, N., Winstead, C., Myers, C.: Stochastic model checking of genetic circuits. J. Emerg. Technol. Comput. Syst. 11(3), 23:1–23:21 (2014). https://doi.org/10.1145/2644817. http://doi.acm.org/10.1145/2644817
Neupane, T., Myers, C.J., Madsen, C., Zheng, H., Zhang, Z.: STAMINA: STochastic approximate model-checker for INfinite-state analysis. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 540–549. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25540-4_31
Neupane, T., Zhang, Z., Madsen, C., Zheng, H., Myers, C.J.: Approximation techniques for stochastic analysis of biological systems. In: Liò, P., Zuliani, P. (eds.) Automated Reasoning for Systems Biology and Medicine. CB, vol. 30, pp. 327–348. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17297-8_12
Nielsen, A.A.K., et al.: Genetic circuit design automation. Science 352(6281), aac7341 (2016). https://doi.org/10.1126/science.aac7341. https://www.science.org/doi/abs/10.1126/science.aac7341
Roberts, R., Neupane, T., Buecherl, L., Myers, C.J., Zhang, Z.: STAMINA 2.0: improving scalability of infinite-state stochastic model checking. In: Finkbeiner, B., Wies, T. (eds.) VMCAI 2022. LNCS, vol. 13182, pp. 319–331. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94583-1_16
Volk, M., Junges, S., Katoen, J.P.: Fast dynamic fault tree analysis by model checking techniques. IEEE Trans. Ind. Informatics 14(1), 370–379 (2018). https://doi.org/10.1109/TII.2017.2710316
Acknowledgment
We thank Tim Quatmann at RWTH Aachen University for his help with interfacing the Storm model checker. This work was supported by the National Science Foundation under Grant No. 1856733, 1856740, and 1900542. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
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Jeppson, J. et al. (2023). STAMINA in C++: Modernizing an Infinite-State Probabilistic Model Checker. In: Jansen, N., Tribastone, M. (eds) Quantitative Evaluation of Systems. QEST 2023. Lecture Notes in Computer Science, vol 14287. Springer, Cham. https://doi.org/10.1007/978-3-031-43835-6_7
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