Stochastic Simulation of Graph Transformation Systems

Torrini, Paolo; Heckel, Reiko; Ráth, István

doi:10.1007/978-3-642-12029-9_11

Cited by 27 publications

(28 citation statements)

References 5 publications

(9 reference statements)

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“…They provide some support using GROOVE [28] and PRISM. In later works [38], they handle distributions depending on pairs rule-match and may perform stocastic simulation. They develop GRaSS, with VIATRA as back-end, which can run multiples simulations limited by a time amount or number of steps.…”

Section: Related Workmentioning

confidence: 99%

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Durán

Moreno-Delgado

Álvarez-Palomo

2016

Fundamental Approaches to Software Engineering

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Abstract. Domain experts may use novel tools that allow them to design and model their systems in a notation very close to the domain problem. However, the use of tools for the statistical analysis of stochastic systems requires software engineers to carefully specify such systems in low level and specific languages. In this work we line up both scenarios, specific domain modeling and statistical analysis. Specifically, we have extended the e-Motions system, a framework to develop real-time domain-specific languages where the behavior is specified in a natural way by in-place transformation rules, to support the statistical analysis of systems defined using it. We discuss how restricted e-Motions systems are used to produce Maude corresponding specifications, using a model transformation from e-Motions to Maude, which comply with the restrictions of the VeStA tool, and which can therefore be used to perform statistical analysis on the stochastic systems thus generated. We illustrate our approach with a very simple messaging distributed system.

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Section: Related Workmentioning

confidence: 99%

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Durán

Moreno-Delgado

Álvarez-Palomo

2016

Fundamental Approaches to Software Engineering

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“…Delays are used in [4,17] to extend GT with time. Once a valid match for a rule is found, the execution of the rule at such match is delayed by a time σ (an interval in [4] and other distributions in [17]). We write these rules as p = L σ → R .…”

Section: Fig 8 Activities (Left) Rules With Delays (Center) Stochmentioning

confidence: 99%

“…The work of [9] takes concepts from stochastic Petri nets, so that rules are assigned a delay given by a negative exponential distribution. An important difference is that, while time is assigned to rules in [4,9,17], we assign it to schedulings. Hence, while they interpret rules as activities with unobservable initiation, we interpret rules as events, making our approach able to model all of them in a unified way.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, while they interpret rules as activities with unobservable initiation, we interpret rules as events, making our approach able to model all of them in a unified way. In [17], events are related to equivalence classes of matches modulo renaming, and time can follow a general distribution. Our approach, based on parameter passing and scheduling, is more efficient as we do not need to compute the equivalence classes at each derivation step.…”

Section: Related Workmentioning

confidence: 99%

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Graph Transformation for Domain-Specific Discrete Event Time Simulation

Lara

Guerra

Boronat

et al. 2010

Lecture Notes in Computer Science

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Abstract. Graph transformation is being increasingly used to express the semantics of domain specific visual languages since its graphical nature makes rules intuitive. However, many application domains require an explicit handling of time in order to represent accurately the behaviour of the real system and to obtain useful simulation metrics.Inspired by the vast knowledge and experience accumulated by the discrete event simulation community, we propose a novel way of adding explicit time to graph transformation rules. In particular, we take the event scheduling discrete simulation world view and incorporate to the rules the ability of scheduling the occurrence of other rules in the future. Hence, our work combines standard, efficient techniques for discrete event simulation (based on the handling of a future event set) and the intu-itive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches.

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“…The syntax and semantics for the visual modelling language has been developed, which allows us to model human resources as part of business processes using a rule-based approach. The model will be implemented in an existing stochastic simulation GT tool [10] to validate performance aspects of the model, such as the probability for cases to finish within their deadlines, comparing different scheduling policies.…”

Section: (R5)mentioning

confidence: 99%

Stochastic Modelling and Simulation of Dynamic Resource Allocation

Donyina

2010

Lecture Notes in Computer Science

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Stochastic Simulation of Graph Transformation Systems

Cited by 27 publications

References 5 publications

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Graph Transformation for Domain-Specific Discrete Event Time Simulation

Stochastic Modelling and Simulation of Dynamic Resource Allocation

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