Improvements in Statistical Model Checking: Estimation, Optimization, Parallelization

Abstract

The size and complexity of the technological solutions we now design to tackle the problems of our modern world have increased tremendously. From medical robots to autonomous cars, the products of innovation must now carry out missions that are not only of critical importance but also come with strict requirements and high expectations. For a long time, engineers have been developing new theories and tools to improve the verification of complex software and large systems. Beyond basic testing and code reviewing, formal methods can offer absolute guarantees with respect to the properties of interest in an automated fashion. However, they suffer from a fundamental limitation: they do not scale well and can seldom be used for real-world applications. Statistical Model Checking (SMC) combines the rigorous framework of standard model checking with sampling and statistical algorithms to avoid that issue. Over the last thirty years, SMC has grown into a mature and promising field of research, yet many challenges remain. This thesis explores multiple paths to improve SMC, providing new insight and solutions to these challenges. We first study adaptive stopping algorithms as an alternative to basic sampling approaches and classic Monte Carlo algorithms. We show how they can significantly reduce the number of simulations that are required for the verification of a system, enhancing the scalability of SMC methods even further. We illustrate their efficiency with the rare event problem. Next, we propose an update to the Smart Sampling algorithm and describe the structure of a genetic algorithm for the optimization of schedulers for Markov decision processes. We then investigate the importance of parallelization as a practical solution to the scalability limitations of SMC. Finally, we expand SMC to a new class of models with temporal networks to analyze the robustness of task networks with data from NASA’s Perseverance rover and from the biomanufacturing industry.(FSA - Sciences de l'ingénieur) -- UCL, 202