Probabilistic Analysis Based On Symbolic Game Semantics and Model Counting

Probabilistic program analysis aims to quantify the probability that a given program satisfies a required property. It has many potential applications, from program understanding and debugging to computing program reliability, compiler optimizations and quantitative information flow analysis for security. In these situations, it is usually more relevant to quantify the probability of satisfying/violating a given property than to just assess the possibility of such events to occur. In this work, we introduce an approach for probabilistic analysis of open programs (i.e. programs with undefined identifiers) based on game semantics and model counting. We use a symbolic representation of algorithmic game semantics to collect the symbolic constraints on the input data (context) that lead to the occurrence of the target events (e.g. satisfaction/violation of a given property). The constraints are then analyzed to quantify how likely is an input to satisfy them. We use model counting techniques to count the number of solutions (from a bounded integer domain) that satisfy given constraints. These counts are then used to assign probabilities to program executions and to assess the probability for the target event to occur at the desired level of confidence. Finally, we present the results of applying our approach to several interesting examples and illustrate the benefits they may offer.Comment: In Proceedings GandALF 2017, arXiv:1709.0176

Quantitative Simulations by Matrices

We introduce notions of simulation between semiring-weighted automata as models of quantitative systems. Our simulations are instances of the categorical/coalgebraic notions previously studied by Hasuo---hence soundness against language inclusion comes for free---but are concretely presented as matrices that are subject to linear inequality constraints. Pervasiveness of these formalisms allows us to exploit existing algorithms in: searching for a simulation, and hence verifying quantitative correctness that is formulated as language inclusion. Transformations of automata that aid search for simulations are introduced, too. This verification workflow is implemented for the plus-times and max-plus semirings. Furthermore, an extension to weighted tree automata is presented and implemented.Comment: Extended version of [Urabe & Hasuo, CONCUR 2014

Algorithmic probabilistic game semantics Playing games with automata

Abstract We present a detailed account of a translation from probabilistic call-by-value in that programs exhibit the same computational behaviour if and only if the corresponding automata are language-equivalent. Since probabilistic language equivalence is decidable, we can apply the translation to analyse the behaviour of probabilistic programs and protocols. We illustrate our approach on a number of case studies