Testing and debugging functional reactive programming

Perez, Ivan; Nilsson, Henrik

doi:10.1145/3110246

Cited by 21 publications

(14 citation statements)

References 27 publications

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“…Recently, Ramson and Hirschfeld [2017] proposed active expressions as a primitive for change detection, upon which different kinds of RP can be built. Other recent research directions in RP include debugging [Banken et al 2018;Perez and Nilsson 2017;Salvaneschi and Mezini 2016], thread-safe concurrency [Drechsler et al 2018], and application to new domains such as IoT and edge computing [Calus et al 2017] and autonomous vehicles [Finkbeiner 129:27 et al 2017]. In the distributed setting, the DREAM reactive middleware Salvaneschi 2014, 2018] allows selecting different levels of consistency guarantees for distributed RP.…”

Section: Performancementioning

confidence: 99%

Distributed system development with ScalaLoci

Weisenburger

Köhler

Salvaneschi

2018

Proc. ACM Program. Lang.

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Distributed applications are traditionally developed as separate modules, often in different languages, which react to events, like user input, and in turn produce new events for the other modules. Separation into components requires time-consuming integration. Manual implementation of communication forces programmers to deal with low-level details. The combination of the two results in obscure distributed data flows scattered among multiple modules, hindering reasoning about the system as a whole. The ScalaLoci distributed programming language addresses these issues with a coherent model based on placement types that enables reasoning about distributed data flows, supporting multiple software architectures via dedicated language features and abstracting over low-level communication details and data conversions. As we show, ScalaLoci simplifies developing distributed systems, reduces error-prone communication code and favors early detection of bugs. CCS Concepts: • Software and its engineering → Distributed programming languages; Domain specific languages; • Theory of computation → Distributed computing models;

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Section: Performancementioning

confidence: 99%

Distributed system development with ScalaLoci

Weisenburger

Köhler

Salvaneschi

2018

Proc. ACM Program. Lang.

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“…To show how arrowized programming works, we implement a simple signal function, which calculates the acceleration of a falling mass on its vertical axis as an example [38].…”

Section: Arrowized Programmingmentioning

confidence: 99%

Pure Functional Epidemics

Thaler

Altenkirch

Siebers

2018

Proceedings of the 30th Symposium on Implementation and Application of Functional Languages

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Agent-Based Simulation (ABS) is a methodology in which a system is simulated in a bottom-up approach by modelling the micro interactions of its constituting parts, called agents, out of which the global system behaviour emerges. So far mainly object-oriented techniques and languages have been used in ABS. Using the SIR model of epidemiology, which simulates the spreading of an infectious disease through a population, we demonstrate how to use pure Functional Reactive Programming to implement ABS. With our approach we can guarantee the reproducibility of the simulation at compile time and rule out specific classes of run-time bugs, something that is not possible with traditional object-oriented languages. Also, we found that the representation in a purely functional format is conceptually quite elegant and opens the way to formally reason about ABS.

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“…We have shown both how to run the system in ideal conditions, and how to use the type-checker to verify that faults are specified. An possible addition would be to extend MSFs with a way of injecting faults [Perez and Nilsson 2017]. This could be additionally improved by the use of a tool like QuickCheck [Claessen and Hughes 2000], that tries to check a property against a series of randomly generated inputs.…”

Section: Future Workmentioning

confidence: 99%

“…For further details on FRP and Arrowized FRP (AFRP), see earlier papers [Courtney et al 2003;Elliott and Hudak 1997;Nilsson et al 2002]. This presentation draws heavily from the summaries in [Courtney et al 2003;Perez and Nilsson 2017]. For details on fault tolerance, see [Avižienis 1967[Avižienis , 1976 and, for an in-depth introduction, see [Butler 2008].…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant functional reactive programming (functional pearl)

Perez

2018

Proc. ACM Program. Lang.

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Highly critical application domains, like medicine and aerospace, require the use of strict design, implementation and validation techniques. Functional languages have been used in these domains to develop synchronous dataflow programming languages for reactive systems. Causal stream functions and Functional Reactive Programming capture the essence of those languages in a way that is both elegant and robust. To guarantee that critical systems can operate under high stress over long periods of time, these applications require clear specifications of possible faults and hazards, and how they are being handled. Modeling failure is straightforward in functional languages, and many Functional Reactive abstractions incorporate support for failure or termination. However, handling unknown types of faults, and incorporating fault tolerance into Functional Reactive Programming, requires a different construction and remains an open problem. This work presents extensions to an existing functional reactive abstraction to facilitate tagging reactive transformations with hazard tags or confidence levels. We present a prototype framework to quantify the reliability of a reactive construction, by means of numeric factors or probability distributions, and demonstrate how to aid the design of fault-tolerant systems, by constraining the allowed reliability to required boundaries. By applying type-level programming, we show that it is possible to improve static analysis and have compiletime guarantees of key aspects of fault tolerance. Our approach is powerful enough to be used in systems with realistic complexity, and flexible enough to be used to guide their analysis and design, to test system properties, to verify fault tolerance properties, to perform runtime monitoring, to implement fault tolerance during execution and to address faults during runtime. We present implementations in Haskell and in Idris. CCS Concepts: • Software and its engineering → Functional languages; • Hardware → Fault tolerance; • Theory of computation → Streaming models;

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Testing and debugging functional reactive programming

Cited by 21 publications

References 27 publications

Distributed system development with ScalaLoci

Distributed system development with ScalaLoci

Pure Functional Epidemics

Fault tolerant functional reactive programming (functional pearl)

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