Classical logic, continuation semantics and abstract machines

One of the goals of this paper is to demonstrate that denotational semantics is useful for operational issues like implementation of functional languages by abstract machines. This is exemplified in a tutorial way by studying the case of extensional untyped call-by-name λ-calculus with Felleisen's control operator 𝒞. We derive the transition rules for an abstract machine from a continuation semantics which appears as a generalization of the ¬¬-translation known from logic. The resulting abstract machine appears as an extension of Krivine's machine implementing head reduction. Though the result, namely Krivine's machine, is well known our method of deriving it from continuation semantics is new and applicable to other languages (as e.g. call-by-value variants). Further new results are that Scott's D∞-models are all instances of continuation models. Moreover, we extend our continuation semantics to Parigot's λμ-calculus from which we derive an extension of Krivine's machine for λμ-calculus. The relation between continuation semantics and the abstract machines is made precise by proving computational adequacy results employing an elegant method introduced by Pitts

Combining Forward and Backward Abstract Interpretation of Horn Clauses

Alternation of forward and backward analyses is a standard technique in abstract interpretation of programs, which is in particular useful when we wish to prove unreachability of some undesired program states. The current state-of-the-art technique for combining forward (bottom-up, in logic programming terms) and backward (top-down) abstract interpretation of Horn clauses is query-answer transformation. It transforms a system of Horn clauses, such that standard forward analysis can propagate constraints both forward, and backward from a goal. Query-answer transformation is effective, but has issues that we wish to address. For that, we introduce a new backward collecting semantics, which is suitable for alternating forward and backward abstract interpretation of Horn clauses. We show how the alternation can be used to prove unreachability of the goal and how every subsequent run of an analysis yields a refined model of the system. Experimentally, we observe that combining forward and backward analyses is important for analysing systems that encode questions about reachability in C programs. In particular, the combination that follows our new semantics improves the precision of our own abstract interpreter, including when compared to a forward analysis of a query-answer-transformed system.Comment: Francesco Ranzato. 24th International Static Analysis Symposium (SAS), Aug 2017, New York City, United States. Springer, Static Analysi

Data refinement for true concurrency

The majority of modern systems exhibit sophisticated concurrent behaviour, where several system components modify and observe the system state with fine-grained atomicity. Many systems (e.g., multi-core processors, real-time controllers) also exhibit truly concurrent behaviour, where multiple events can occur simultaneously. This paper presents data refinement defined in terms of an interval-based framework, which includes high-level operators that capture non-deterministic expression evaluation. By modifying the type of an interval, our theory may be specialised to cover data refinement of both discrete and continuous systems. We present an interval-based encoding of forward simulation, then prove that our forward simulation rule is sound with respect to our data refinement definition. A number of rules for decomposing forward simulation proofs over both sequential and parallel composition are developed

Rewriting Logic Semantics of a Plan Execution Language

The Plan Execution Interchange Language (PLEXIL) is a synchronous language developed by NASA to support autonomous spacecraft operations. In this paper, we propose a rewriting logic semantics of PLEXIL in Maude, a high-performance logical engine. The rewriting logic semantics is by itself a formal interpreter of the language and can be used as a semantic benchmark for the implementation of PLEXIL executives. The implementation in Maude has the additional benefit of making available to PLEXIL designers and developers all the formal analysis and verification tools provided by Maude. The formalization of the PLEXIL semantics in rewriting logic poses an interesting challenge due to the synchronous nature of the language and the prioritized rules defining its semantics. To overcome this difficulty, we propose a general procedure for simulating synchronous set relations in rewriting logic that is sound and, for deterministic relations, complete. We also report on two issues at the design level of the original PLEXIL semantics that were identified with the help of the executable specification in Maude

Extending and Relating Semantic Models of Compensating CSP

Business transactions involve multiple partners coordinating and interacting with each other. These transactions have hierarchies of activities which need to be orchestrated. Usual database approaches (e.g.,checkpoint, rollback) are not applicable to handle faults in a long running transaction due to interaction with multiple partners. The compensation mechanism handles faults that can arise in a long running transaction. Based on the framework of Hoare's CSP process algebra, Butler et al introduced Compensating CSP (cCSP), a language to model long-running transactions. The language introduces a method to declare a transaction as a process and it has constructs for orchestration of compensation. Butler et al also defines a trace semantics for cCSP. In this thesis, the semantic models of compensating CSP are extended by defining an operational semantics, describing how the state of a program changes during its execution. The semantics is encoded into Prolog to animate the specification. The semantic models are further extended to define the synchronisation of processes. The notion of partial behaviour is defined to model the behaviour of deadlock that arises during process synchronisation. A correspondence relationship is then defined between the semantic models and proved by using structural induction. Proving the correspondence means that any of the presentation can be accepted as a primary definition of the meaning of the language and each definition can be used correctly at different times, and for different purposes. The semantic models and their relationships are mechanised by using the theorem prover PVS. The semantic models are embedded in PVS by using Shallow embedding. The relationships between semantic models are proved by mutual structural induction. The mechanisation overcomes the problems in hand proofs and improves the scalability of the approach