Towards Abstraction-based Probabilistic Program Analysis - Szekeres, Dániel; Majzik, István (Acta Cybernetica, 2023)
Probabilistic programs that can represent both probabilistic and non-deterministic choices are useful for creating reliability models of complex safety-critical systems that interact with humans or external systems. Such models are often quite complex, so their analysis can be hindered by state-space explosion. One common approach to deal with this problem is the application of abstraction techniques. We present improvements for an abstraction-refinement scheme for the analysis of probabilistic programs, aiming to improve the scalability of the scheme by adapting modern techniques from qualitative software model checking, and make the analysis result more reliable using better convergence checks. We implemented and evaluated the improvements in our Theta model checking framework.