The authors present a novel control-flow refinement (CFR) technique that can be used to improve the automated complexity analysis of probabilistic integer programs. They prove the soundness of their CFR approach and demonstrate its benefits by implementing it in their complexity analysis tool KoAT.
The authors present a new method for static equivalence and similarity refutation analyses of probabilistic program pairs. The method is fully automated, applicable to infinite-state probabilistic programs, and provides formal guarantees on the correctness of its results.