Abstract
AbstractStatistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this article, we present : a statistical model checker that combines fully automated importance splitting to estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it supports a variety of input formalisms natively and via the Jani exchange format. A modular software architecture allows its various features to be flexibly combined. We highlight its capabilities using experiments across multi-core and distributed setups on three case studies and report on an extensive performance comparison with three current statistical model checkers.
Publisher
Springer Science and Business Media LLC
Subject
Information Systems,Software
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. The Best of Both Worlds: Analytically-Guided Simulation of HPnGs for Optimal Reachability;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024
2. Optimized Smart Sampling;Bridging the Gap Between AI and Reality;2023-12-14
3. Shielded Learning for Resilience and Performance Based on Statistical Model Checking in Simulink;Bridging the Gap Between AI and Reality;2023-12-14
4. Optimal Route Synthesis in Space DTN Using Markov Decision Processes;Theoretical Aspects of Computing – ICTAC 2023;2023
5. Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis;2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS);2022-05