Jürgen Giesl

Jan-Christoph Kassing, Henri Nagel, Alexander Schlecht et al.

In recent years, numerous techniques were developed to automatically prove termination of different kinds of probabilistic programs. However, there are only few automated methods to disprove their termination. In this paper, we present the first techniques to automatically disprove (positive) almost-sure termination of probabilistic term rewrite systems. Disproving termination of non-probabilistic systems requires finding a finite representation of an infinite computation, e.g., a loop of the rewrite system. We extend such qualitative techniques to probabilistic term rewriting, where a quantitative analysis is required. In addition to the existence of a loop, we have to count the number of such loops in order to embed suitable random walks into a computation, thereby disproving termination. To evaluate their power, we implemented all our techniques in the tool AProVE.

Florian Frohn, Jürgen Giesl

We propose a new approach for proving safety of infinite state systems. It extends the analyzed system by transitive relations until its diameter D becomes finite, i.e., until constantly many steps suffice to cover all reachable states, irrespective of the initial state. Then we can prove safety by checking that no error state is reachable in D steps. To deduce transitive relations, we use recurrence analysis. While recurrence analyses can usually find conjunctive relations only, our approach also discovers disjunctive relations by combining recurrence analysis with projections. An empirical evaluation of the implementation of our approach in our tool LoAT shows that it is highly competitive with the state of the art.

Florian Frohn, Jürgen Giesl

We propose a novel acceleration technique for loops operating on arrays. The goal of acceleration is to characterize the transitive closure of loops in a logic which is suitable for automated reasoning. Using the new notion of inductive lvalues, our technique can handle loops where previous techniques fail, and it unifies acceleration for arrays and scalar variables by regarding scalars as arrays of dimension 0. Moreover, our approach uses λs instead of quantifiers. Then the resulting SMT problems can be solved via lemmas on demand. An empirical evaluation of our implementation in the tool LoAT shows the power of our approach.