Imperial College London, and CONICET Model-based reliability estimation of systems can provide useful insights early in the development process. However, computational complexity of estimating metrics such as mean time to first failure (MTTFF), turnaround time (TAT), or other domain-based quantitative measures can be prohibitive both in time, space, and precision. In this article, we present an alternative to exhaustive model exploration, as in probabilistic model checking, and partial random exploration, as in statistical model checking. Our hypothesis is that a (carefully crafted) partial systematic exploration of a system model can provide better bounds for these quantitative model metrics at lower computation cost. We present a novel automated technique for metric estimation that combines simulation, invariant inference, and probabilistic model checking. Simulation produces a probabilistically relevant set of traces from which a state invariant is inferred. The invariant characterises a partial model, which is then exhaustively explored using probabilistic model checking. We report on experiments that suggest that metric estimation using this technique (for both fully probabilistic models and those exhibiting nondeterminism) can be more effective than (full-model) probabilistic and statistical model checking, especially for system models for which the events of interest are rare. CCS Concepts: r Software and its engineering → Software performance; Software reliability; Additional Key Words and Phrases: Quantitative modelling, probability, model checking, partial verification, estimation ACM Reference Format: Esteban Pavese, Víctor Braberman, and Sebastian Uchitel. 2016. Less is more: Estimating probabilistic rewards over partial system explorations.
Pavese, Barberman, and Uchitel described how to develop partial explorations of a system model automatically REF .
Less is More: Estimating Probabilistic Rewards over Partial System Explorations
TSEM
Computer Science