Integrating stochastic reasoning into Event-B development

Tarasyuk, Anton; Troubitsynå, Elena; Laibinis, Linas

doi:10.1007/s00165-014-0305-z

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…In Event-B, non-determinism occurs in several places and, to the best of our knowledge, existing works on extending Event-B with probabilities have only focused on refining non-deterministic assignments into probabilistic assignments [9,20,22] while leaving other sources of non-determinism such as the choice between enabled events or the choice between admissible parameter values untouched.…”

Section: Resultsmentioning

confidence: 99%

“…In [25], Yilmaz study the refinement of qualitative probabilistic Event-B models and propose a tool support inside Rodin. Other works [20,22,21] have extended this approach by refining non-deterministic assignments into quantitative probabilistic assignments where, unlike in [9], the actual probability values are specified. This new proposition is then exploited in order to assess several system properties such as reliability and responsiveness.…”

Section: Introductionmentioning

confidence: 99%

“…In this first extension, Hallerstede and Hoang focus on a qualitative aspect of probabilities and adapt the Event-B semantics and proof obligations to this new setting. Quantitative probabilistic assignments are then introduced in [20,22,21]. Almostcertain convergence of a set of events is studied in [9] and [12] in this context of non-deterministic models with probabilistic assignments.…”

Section: Introductionmentioning

confidence: 99%

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Moving from Event-B to probabilistic Event-B

Amine

Delahaye

Lanoix

2017

Proceedings of the Symposium on Applied Computing

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We propose a fully probabilistic extension of Event-B where all the non-deterministic choices are replaced with probabilities. We present the syntax and the semantics of this extension and introduce novel and adapted proof obligations for proving the correctness of probabilistic Event-B models. As a preliminary step towards handling refinement of probabilistic Event-B models, we propose sufficient conditions for the almost-certain convergence of a set of events and express them in terms of proof obligations. We illustrate our work by presenting a case study specified in both standard and probabilistic Event-B.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving from Event-B to probabilistic Event-B

Amine

Delahaye

Lanoix

2017

Proceedings of the Symposium on Applied Computing

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“…In [25], the same authors study the refinement of qualitative probabilistic Event-B models and propose tool support inside Rodin. Other works [26,27,28] have extended this approach by refining non-deterministic assignments into quantitative probabilistic assignments where, unlike in [24], the actual probability values are specified. This new proposition is then exploited in order to assess several system properties such as reliability and responsiveness.…”

Section: Introductionmentioning

confidence: 99%

Reliability in Fully Probabilistic Event-B: How to Bound the Enabling of Events

Aouadi

Lanoix

2018

Communications in Computer and Information Science

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In previous work, we have proposed a fully probabilistic version of Event-B where all the non-deterministic choices are replaced by probabilistic ones and, particularly, the events are equipped with weights that allow us to consider their enabling probability. In this work, we focus on the reliability of the system by proposing to constraint the probability of enabling an event (or a set of events) to control its importance with regard to the intended system behaviour. We add a specific upper bound which must limit the enabling probabilities of the chosen events and we consider the necessary proof obligations to check that the considered events respect the bound. At the end, we illustrate our work by presenting a case study specified in probabilistic Event-B and where bounding the enabling of some events is mandatory.

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“…In [40], the same authors study the refinement of qualitative probabilistic Event-B models and propose a tool support inside Rodin. Other works [35,37,36] have extended this approach by refining non-deterministic assignments into quantitative probabilistic assignments where, unlike in [20], the actual probability values are specified. This new proposition is then exploited in order to assess several system properties such as reliability and responsiveness.…”

Section: Introductionmentioning

confidence: 99%

Introducing probabilistic reasoning within Event-B

2017

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Event-B is a proof-based formal method used for discrete systems modelling. Several works have previously focused on the extension of Event-B for the description of probabilistic systems. In this paper, we propose an extension of Event-B that allows designing fully probabilistic systems as well as systems containing both probabilistic and non-deterministic choices. Compared to existing approaches which only focus on probabilistic assignments, our approach allows expressing probabilistic choices in all places where non-deterministic choices originally appear in a standard Event-B model: in the choice between enabled events, event-parameter values and in probabilistic assignments. Furthermore, we introduce novel and adapted proof-obligations for the consistency of such systems and introduce two key aspects to incremental design: probabilisation of existing events and refinement through the addition of new probabilistic events. In particular, we provide proof-obligations for the almost-certain convergence of a set of new events, which is a required property in order to prove standard refinement in this context. Finally, we propose a fully detailed case study, which we use throughout the paper to illustrate our new constructions. Non-determinism probabilisation refinement non-det. model proba. model refinement non-det. model non-det. model proba. refinement proba. model proba. model proba. model probabilisation partial probabilisation proba. refinement proba. refinement proba. refinement non-det. model mixed model partial. probabilisation mixed model mixed model refinement proba. refinement

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Integrating stochastic reasoning into Event-B development

Cited by 21 publications

References 25 publications

Moving from Event-B to probabilistic Event-B

Moving from Event-B to probabilistic Event-B

Reliability in Fully Probabilistic Event-B: How to Bound the Enabling of Events

Introducing probabilistic reasoning within Event-B

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