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This paper presents the first slicing approach for probabilistic programs based on specifications. We show that when probabilistic programs are accompanied by their specifications in the form of pre- and post-condition, we can exploit this semantic information to produce specification-preserving slices strictly more precise than slices yielded by conventional techniques based on data/control dependency. To achieve this goal, our technique is based on the backward propagation of post-conditions via the greatest pre-expectation transformer -- the probabilistic counterpart of Dijkstra weakest pre-condition transformer. The technique is termination-sensitive, allowing to preserve the partial as well as the total correctness of probabilistic programs w.r.t. their specifications. It is modular, featuring a local reasoning principle, and is formally proved correct. As fundamental technical ingredients of our technique, we design and prove sound verification condition generators for establishing the partial and total correctness of probabilistic programs, which are of interest on their own and can be exploited elsewhere for other purposes. On the practical side, we demonstrate the applicability of our approach by means of a few illustrative examples and a case study from the probabilistic modelling field. We also describe an algorithm for computing least slices among the space of slices derived by our technique.

Language support for differentially-private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system based approaches using linear types tend to be more lightweight and amenable to automatic checking and inference, and in particular in the presence of higher-order programming. Since the seminal design of Fuzz, which is restricted to $\epsilon$-differential privacy in its original design, significant progress has been made to support more advancedvariants of differential privacy, like($\epsilon$,$\delta$)-differential privacy. However, supporting these advanced privacy variants while also supporting higher-order programming in full has proven to be challenging. We present Jazz, a language and type system which uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher-order programming. Latent contextual effects allow delaying the payment of effects for connectives such as products, sums and functions, yielding advantages in terms of precision of the analysis and annotation burden upon elimination, as well as modularity. We formalize the core of Jazz, prove it sound for privacy via a logical relation for metric preservation, and illustrate its expressive power through a number of case studies drawn from the recent differential privacy literature., Comment: Published in Transactions on Programming Languages and Systems (TOPLAS) 2023

In this abstract we study the resource consumption of quantum programs. Specifically, we focus on the expected runtime of programs and, inspired by recent methods for probabilistic programs, we develop a calculus \`a la weakest precondition to formally and systematically derive the (exact) expected runtime of quantum programs. Notably, the calculus admits a notion of loop runtime invariant that can be readily used to derive upper bounds of their runtime. Finally, we show the applicability of our calculus analyzing the runtime of (a simplified version of) the BB84 quantum key distribution protocol., Comment: Accepted at PLanQC 2020

This paper presents a wp-style calculus for obtaining expectations on the outcomes of (mutually) recursive probabilistic programs. We provide several proof rules to derive one-- and two--sided bounds for such expectations, and show the soundness of our wp-calculus with respect to a probabilistic pushdown automaton semantics. We also give a wp-style calculus for obtaining bounds on the expected runtime of recursive programs that can be used to determine the (possibly infinite) time until termination of such programs.

This paper presents a wp-style calculus for obtaining bounds on the expected run-time of probabilistic programs. Its application includes determining the (possibly infinite) expected termination time of a probabilistic program and proving positive almost-sure termination - does a program terminate with probability one in finite expected time? We provide several proof rules for bounding the run-time of loops, and prove the soundness of the approach with respect to a simple operational model. We show that our approach is a conservative extension of Nielson's approach for reasoning about the run-time of deterministic programs. We analyze the expected run-time of some example programs including a one-dimensional random walk and the coupon collector problem.

We investigate the semantic intricacies of conditioning, a main feature in probabilistic programming. We provide a weakest (liberal) pre-condition (w(l)p) semantics for the elementary probabilistic programming language pGCL extended with conditioning. We prove that quantitative weakest (liberal) pre-conditions coincide with conditional (liberal) expected rewards in Markov chains and show that semantically conditioning is a truly conservative extension. We present two program transformations which entirely eliminate conditioning from any program and prove their correctness using the w(l)p-semantics. Finally, we show how the w(l)p-semantics can be used to determine conditional probabilities in a parametric anonymity protocol and show that an inductive w(l)p-semantics for conditioning in non-deterministic probabilistic programs cannot exist.