Rule-Based Form for Stream Constraints

Dokter, Kasper; Arbab, Farhad

doi:10.1007/978-3-319-92408-3_6

Cited by 4 publications

(4 citation statements)

References 22 publications

(32 reference statements)

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“…Programs in ARx express functions from streams to streams, and describe the (non-strict) order between consuming and producing values. ARx's semantics is given by stream builders, defined below, which are closely inspired on Dokter and Arbab's stream constraints [10].…”

Section: Stream Builders: Syntaxmentioning

confidence: 99%

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ARx: Reactive Programming for Synchronous Connectors

Proença¹,

Cledou

2020

Lecture Notes in Computer Science

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Reactive programming (RP) languages and Synchronous Coordination (SC) languages share the goal of orchestrating the execution of computational tasks, by imposing dependencies on their execution order and controlling how they share data. RP is often implemented as libraries for existing programming languages, lifting operations over values to operations over streams of values, and providing efficient solutions to manage how updates to such streams trigger reactions, i.e., the execution of dependent tasks. SC is often implemented as a standalone formalism to specify existing component-based architectures, used to analyse, verify, transform, or generate code. These two approaches target different audiences, and it is non-trivial to combine the programming style of RP with the expressive power of synchronous languages.This paper proposes a lightweight programming language to describe component-based Architectures for Reactive systems, dubbed ARx, which blends concepts from RP and SC, mainly inspired to the Reo coordination language and its composition operation, and with tailored constructs for reactive programs such as the ones found in ReScala. ARx is enriched with a type system and with algebraic data types, and has a reactive semantics inspired in RP. We provide typical examples from both the RP and SC literature, illustrate how these can be captured by the proposed language, and describe a web-based prototype tool to edit, parse, and type check programs, and to animate their semantics.Reactive programming languages, such as Yampa [14], ReScala [11], and Angular 1 , address how to lift traditional functions from concrete data values to 1 https://angular.io/.

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Section: Stream Builders: Syntaxmentioning

confidence: 99%

“…The semantics of an ARx program is given by a guarded command language, which we call stream builders (Sect. 4.2), following the ideas from Dokter and Arbab's stream constraints [10]. An instance of this intermediate language is illustrated in 3 , and includes not only stream variables from ARx, but also memory variables (e.g., m0).…”

Section: Introductionmentioning

confidence: 99%

ARx: Reactive Programming for Synchronous Connectors

Proença¹,

Cledou

2020

Lecture Notes in Computer Science

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“…Some semantics for Reo components, such as constraint automata, suffer from a state space explosion, and several optimizations have been studied [28]. In this work, we use a logical semantics (presented in Section 7.3) to define the behavior of atomic components, which, under certain conditions, keeps the composite representation linear [15].…”

Section: Reo As Candidatementioning

confidence: 99%

“…Internally, the compiler normalizes the composite formula, and gets back a set of synchronous clauses. The notion of synchronous clause is left intuitive here, and can be found with more details in [15]. Essentially, the compiler uses the information present in each clause, such as whether a port must fire or not, to distribute the formula efficiently.…”

Section: Reo To Maudementioning

confidence: 99%

Soft component automata: Composition, compilation, logic, and verification

Kappé

Lion

Arbab

et al. 2019

Science of Computer Programming

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The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a system that meaningfully exhibits their collective behavior. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using algebraic structures on preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system, exhibiting intuitively meaningful behavior. We extend linear temporal logic with two unary connectives that reflect the compositional structure of actions, and show that it is decidable whether all behaviors of a given automaton satisfy a formula of this extended logic. We then show how this logic can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components. Lastly, we implement a toolchain that compiles our automata to Maude, allowing us to apply the rich model checking capability of Maude to verify agent behavior.

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