Automated Termination Analysis of Polynomial Probabilistic Programs

TL;DR

This paper introduces Amber, an automated tool for analyzing the termination of polynomial probabilistic programs by extending proof rules and effectively computing polynomial bounds, enabling the verification of almost sure and positive termination.

Contribution

It develops a fully automated approach for termination analysis of polynomial probabilistic programs, extending existing proof rules and implementing them in the Amber tool.

Findings

Amber successfully verifies termination properties of complex probabilistic programs.

The generalized proof rules improve the scope of programs that can be analyzed.

Experimental results demonstrate Amber's effectiveness over existing tools.

Abstract

The termination behavior of probabilistic programs depends on the outcomes of random assignments. Almost sure termination (AST) is concerned with the question whether a program terminates with probability one on all possible inputs. Positive almost sure termination (PAST) focuses on termination in a finite expected number of steps. This paper presents a fully automated approach to the termination analysis of probabilistic while-programs whose guards and expressions are polynomial expressions. As proving (positive) AST is undecidable in general, existing proof rules typically provide sufficient conditions. These conditions mostly involve constraints on supermartingales. We consider four proof rules from the literature and extend these with generalizations of existing proof rules for (P)AST. We automate the resulting set of proof rules by effectively computing asymptotic bounds on polynomials over the program variables. These bounds are used to decide the sufficient conditions - including the constraints on supermartingales - of a proof rule. Our software tool Amber can thus check AST, PAST, as well as their negations for a large class of polynomial probabilistic programs, while carrying out the termination reasoning fully with polynomial witnesses. Experimental results show the merits of our generalized proof rules and demonstrate that Amber can handle probabilistic programs that are out of reach for other state-of-the-art tools.