A Faithful Semantics for Generalised Symbolic Trajectory Evaluation

Generalised Symbolic Trajectory Evaluation (GSTE) is a high-capacity formal verification technique for hardware. GSTE uses abstraction, meaning that details of the circuit behaviour are removed from the circuit model. A semantics for GSTE can be used to predict and understand why certain circuit properties can or cannot be proven by GSTE. Several semantics have been described for GSTE. These semantics, however, are not faithful to the proving power of GSTE-algorithms, that is, the GSTE-algorithms are incomplete with respect to the semantics. The abstraction used in GSTE makes it hard to understand why a specific property can, or cannot, be proven by GSTE. The semantics mentioned above cannot help the user in doing so. The contribution of this paper is a faithful semantics for GSTE. That is, we give a simple formal theory that deems a property to be true if-and-only-if the property can be proven by a GSTE-model checker. We prove that the GSTE algorithm is sound and complete with respect to this semantics

An Abstract Interpretation-based Model of Tracing Just-In-Time Compilation

Tracing just-in-time compilation is a popular compilation technique for the efficient implementation of dynamic languages, which is commonly used for JavaScript, Python and PHP. We provide a formal model of tracing JIT compilation of programs using abstract interpretation. Hot path detection corresponds to an abstraction of the trace semantics of the program. The optimization phase corresponds to a transform of the original program that preserves its trace semantics up to an observation modeled by some abstraction. We provide a generic framework to express dynamic optimizations and prove them correct. We instantiate it to prove the correctness of dynamic type specialization and constant variable folding. We show that our framework is more general than the model of tracing compilation introduced by Guo and Palsberg [2011] based on operational bisimulations.Comment: To appear in ACM Transactions on Programming Languages and System

Causal Consistency for Reversible Multiparty Protocols

In programming models with a reversible semantics, computational steps can be undone. This paper addresses the integration of reversible semantics into process languages for communication-centric systems equipped with behavioral types. In prior work, we introduced a monitors-as-memories approach to seamlessly integrate reversible semantics into a process model in which concurrency is governed by session types (a class of behavioral types), covering binary (two-party) protocols with synchronous communication. The applicability and expressiveness of the binary setting, however, is limited. Here we extend our approach, and use it to define reversible semantics for an expressive process model that accounts for multiparty (n-party) protocols, asynchronous communication, decoupled rollbacks, and abstraction passing. As main result, we prove that our reversible semantics for multiparty protocols is causally-consistent. A key technical ingredient in our developments is an alternative reversible semantics with atomic rollbacks, which is conceptually simple and is shown to characterize decoupled rollbacks.Comment: Extended, revised version of a PPDP'17 paper (https://doi.org/10.1145/3131851.3131864

Metric Semantics and Full Abstractness for Action Refinement and Probabilistic Choice

This paper provides a case-study in the field of metric semantics for probabilistic programming. Both an operational and a denotational semantics are presented for an abstract process language L_pr, which features action refinement and probabilistic choice. The two models are constructed in the setting of complete ultrametric spaces, here based on probability measures of compact support over sequences of actions. It is shown that the standard toolkit for metric semantics works well in the probabilistic context of L_pr, e.g. in establishing the correctness of the denotational semantics with respect to the operational one. In addition, it is shown how the method of proving full abstraction --as proposed recently by the authors for a nondeterministic language with action refinement-- can be adapted to deal with the probabilistic language L_pr as well