Anvesh Komuravelli

Anvesh Komuravelli, Nikolaj Bjorner, Arie Gurfinkel et al.

We present a compositional SMT-based algorithm for safety of procedural C programs that takes the heap into consideration as well. Existing SMT-based approaches are either largely restricted to handling linear arithmetic operations and properties, or are non-compositional. We use Constrained Horn Clauses (CHCs) to represent the verification conditions where the memory operations are modeled using the extensional theory of arrays (ARR). First, we describe an exponential time quantifier elimination (QE) algorithm for ARR which can introduce new quantifiers of the index and value sorts. Second, we adapt the QE algorithm to efficiently obtain under-approximations using models, resulting in a polynomial time Model Based Projection (MBP) algorithm. Third, we integrate the MBP algorithm into the framework of compositional reasoning of procedural programs using may and must summaries recently proposed by us. Our solutions to the CHCs are currently restricted to quantifier-free formulas. Finally, we describe our practical experience over SV-COMP'15 benchmarks using an implementation in the tool SPACER.

Anvesh Komuravelli, Corina S. Pasareanu, Edmund M. Clarke

We consider the problem of learning a non-deterministic probabilistic system consistent with a given finite set of positive and negative tree samples. Consistency is defined with respect to strong simulation conformance. We propose learning algorithms that use traditional and a new "stochastic" state-space partitioning, the latter resulting in the minimum number of states. We then use them to solve the problem of "active learning", that uses a knowledgeable teacher to generate samples as counterexamples to simulation equivalence queries. We show that the problem is undecidable in general, but that it becomes decidable under a suitable condition on the teacher which comes naturally from the way samples are generated from failed simulation checks. The latter problem is shown to be undecidable if we impose an additional condition on the learner to always conjecture a "minimum state" hypothesis. We therefore propose a semi-algorithm using stochastic partitions. Finally, we apply the proposed (semi-) algorithms to infer intermediate assumptions in an automated assume-guarantee verification framework for probabilistic systems.