A Recipe for State-and-Effect Triangles

In the semantics of programming languages one can view programs as state transformers, or as predicate transformers. Recently the author has introduced state-and-effect triangles which capture this situation categorically, involving an adjunction between state- and predicate-transformers. The current paper exploits a classical result in category theory, part of Jon Beck's monadicity theorem, to systematically construct such a state-and-effect triangle from an adjunction. The power of this construction is illustrated in many examples, covering many monads occurring in program semantics, including (probabilistic) power domains

Healthiness Conditions for Predicate Transformers

AbstractThe behavior of a program can be modeled by describing how it transforms input states to output states, the state transformer semantics. Alternatively, for verification purposes one is interested in a 'predicate transformer semantics' which, for every condition on the output, yields the weakest precondition on the input that guarantees the desired property for the output.In the presence of computational effects like nondeterministic or probabilistic choice, a computation will be modeled by a map t:X→TY, where T is an appropriate computational monad. The corresponding predicate transformer assigns predicates on Y to predicates on X. One looks for necessary and, if possible, sufficient conditions (healthiness conditions) on predicate transformers that correspond to state transformers t:X→TY.In this paper we propose a framework for establishing healthiness conditions for predicate transformers. As far as the author knows, it fits to almost all situations in which healthiness conditions for predicate transformers have been worked out. It may serve as a guideline for finding new results; but it also shows quite narrow limitations

An expectation transformer approach to predicate abstraction and data independence for probabilistic programs

In this paper we revisit the well-known technique of predicate abstraction to characterise performance attributes of system models incorporating probability. We recast the theory using expectation transformers, and identify transformer properties which correspond to abstractions that yield nevertheless exact bound on the performance of infinite state probabilistic systems. In addition, we extend the developed technique to the special case of "data independent" programs incorporating probability. Finally, we demonstrate the subtleness of the extended technique by using the PRISM model checking tool to analyse an infinite state protocol, obtaining exact bounds on its performance

A New Proof Rule for Almost-Sure Termination

An important question for a probabilistic program is whether the probability mass of all its diverging runs is zero, that is that it terminates "almost surely". Proving that can be hard, and this paper presents a new method for doing so; it is expressed in a program logic, and so applies directly to source code. The programs may contain both probabilistic- and demonic choice, and the probabilistic choices may depend on the current state. As do other researchers, we use variant functions (a.k.a. "super-martingales") that are real-valued and probabilistically might decrease on each loop iteration; but our key innovation is that the amount as well as the probability of the decrease are parametric. We prove the soundness of the new rule, indicate where its applicability goes beyond existing rules, and explain its connection to classical results on denumerable (non-demonic) Markov chains.Comment: V1 to appear in PoPL18. This version collects some existing text into new example subsection 5.5 and adds a new example 5.6 and makes further remarks about uncountable branching. The new example 5.6 relates to work on lexicographic termination methods, also to appear in PoPL18 [Agrawal et al, 2018

Healthiness from Duality

Healthiness is a good old question in program logics that dates back to Dijkstra. It asks for an intrinsic characterization of those predicate transformers which arise as the (backward) interpretation of a certain class of programs. There are several results known for healthiness conditions: for deterministic programs, nondeterministic ones, probabilistic ones, etc. Building upon our previous works on so-called state-and-effect triangles, we contribute a unified categorical framework for investigating healthiness conditions. We find the framework to be centered around a dual adjunction induced by a dualizing object, together with our notion of relative Eilenberg-Moore algebra playing fundamental roles too. The latter notion seems interesting in its own right in the context of monads, Lawvere theories and enriched categories.Comment: 13 pages, Extended version with appendices of a paper accepted to LICS 201

Quantitative program logic and expected time bounds in probabilistic distributed algorithms

AbstractIn this paper we show how quantitative program logic (Morgan et al., ACM Trans. Programming Languages Systems 18 (1996) 325) provides a formal framework in which to promote standard techniques of program analysis to a context where probability and nondeterminism interact, a situation common to probabilistic distributed algorithms. We show that overall expected time can be formulated directly in the logic and that it can be derived from local properties of components. We illustrate the methods with an analysis of expected running time of the probabilistic dining philosophers (Lehmann and Ravin, Proc 8th Annu. ACM. Symp. on principles of Programming Languages, ACM, New York, 1981, p. 133)