Advanced Methods for Synthesis of Probabilistic Programs

Probabilistic programs play a crucial role in various engineering domains, including computer networks, embedded systems, power management policies, or software product lines. PAYNT is a tool for the automatic synthesis of probabilistic programs satisfying the given specifications. In this thesis, we extend this tool primarily to support optimal synthesis and synthesis for multi-property specifications. Further, we have proposed and implemented a new method that can efficiently synthesise continuous parameters affecting the transition probabilities alongside the synthesis of a program topology, i.e., support of both topology and parameter synthesis at the same time. We demonstrate the usefulness and performance of PAYNT on a wide range of real-world case studies from various application domains. For challenging synthesis problems, PAYNT can significantly decrease the run-time from days to minutes while ensuring the completeness of the synthesis process.

automated synthesis, probabilistic programs, Markov models, model checking