Modeling and verifying dynamic communication structures based on graph transformations

Eckardt, Tobias; Heinzemann, Christian; Henkler, Stefan; Hirsch, Martin; Priesterjahn, Claudia; Schäfer, Wilhelm

doi:10.1007/s00450-011-0184-y

Cited by 25 publications

(9 citation statements)

References 19 publications

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“…Another aspect is the support of the verification of system's behavior specified in the models of multiple disciplines by combining results of verification activities both on the feedback control as well as on the real-time mode management to prove system wide properties. Finally, the MechatronicUML provides specifically support for systems which self-adapt their behavior at runtime by modeling adaptations by architectural reconfiguration based on graph transformations (Eckardt et al, 2013;Tichy et al, 2008). Verification of the adaptation behavior is based on graph transformation verification approaches (Becker et al, 2006;Ghamaraian et al, 2012).…”

Section: State Of the Artmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

253

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a b s t r a c tCoping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

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Section: State Of the Artmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

253

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“…We specify the communication protocol using real-time coordination protocols of MECHATRONICUML ( D.M.f.I.T.S, [55], Sect. 5.2, [42]). Real-time coordination protocols use a state-based behavior specification technique called real-time statecharts to define this message exchange.…”

Section: Profile Distributionmentioning

confidence: 95%

Methods of Improving the Dependability of Self-optimizing Systems

Dorociak

Gausemeier

2014

Lecture Notes in Mechanical Engineering

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Abstract. Various methods have been developed in the Collaborative ResearchCenter 614 which can be used to improve the dependability of self-optimizing systems. These methods are presented in this chapter. They are sorted into two categories with regard to the development process of self-optimizing systems. On one hand, there are methods which can be applied during the Conceptual Design Phase. On the other hand, there are methods that are applicable during Design and Development.There are domain-spanning methods as well as methods that have been specifically developed for particular domains, e.g., software engineering or control engineering. The methods address different attributes of dependability, such as reliability, availability or safety.Each section is prefaced with a short overview of the classification of the described method regarding the corresponding domain(s), as well as its dependability attributes, to provide the reader with a brief outline of the methods' areas of application. Information about independently applicable methods or existing relationships and interactions with other methods or third-party literature is also provided.The development process for self-optimizing mechatronic systems which was introduced in Chap. 2 consists of two main phases: Conceptual Design and Design and Development. The main result of the Conceptual Design is the Principle Solution, which includes all information required for the concrete development during the second phase. Conceptual Design PhaseEven as early as during the specification of the Principle Solution, the dependability of self-optimizing mechatronic systems can be evaluated and improved by employing appropriate methods. The result is an improved Principle Solution, as such methods take the whole system into account before splitting it up into domain-specific tasks for the following development phase.

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“…In this way, we can pass the context of execution to the scheduled events. The advantage of this feature with respect to rule-based approaches like [12,13,22,23,36,45] is that we gain in efficiency, as matches are not sought from scratch but they are just completed starting from the received match. A more subtle difference with other languages for control flow [2,17,19], is that our invocation edges are enabling conditions for the application of rules, but the flow grammars are not to be interpreted as flow charts in traditional programming [3].…”

Section: Discussionmentioning

confidence: 99%

“…Instead, our behavioural specifications are decoupled from the actual system state where they are executed, allowing its dynamic change. This is a distinguishing feature of our approach (and those based on GT [13,23,36,44]) with respect to approaches like that of Ptolemy II [26,16], where dynamic structure changes are difficult to model but they can be naturally expressed using GT rules. See the next subsection for a more detailed comparison with Ptolemy II.…”

Section: Related Workmentioning

confidence: 99%

“…Rule concurrency issues are difficult to handle and have to be solved in an ad-hoc way, whereas we use cancelling edges and the theory of GT to eliminate scheduled matches that are no longer valid. In [13], the authors present an approach for the modelling and verification of time-dependent dynamic structures based on real-time statecharts, where operations with side effects are modelled as graph transformations. Models are extended with clocks, and rules may add or query clock instances using simple inequalities.…”

Section: Related Workmentioning

confidence: 99%

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Domain-specific discrete event modelling and simulation using graph transformation

et al. 2012

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription 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 to accurately represent the behaviour of a real system and to obtain useful simulation metrics in order to measure throughputs, utilization times and average delays. 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 provide rules with the ability to schedule 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 intuitive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches and provide an implementation on top of the rewriting logic system Maude.

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Modeling and verifying dynamic communication structures based on graph transformations

Cited by 25 publications

References 19 publications

Evolution of software in automated production systems: Challenges and research directions

Evolution of software in automated production systems: Challenges and research directions

Methods of Improving the Dependability of Self-optimizing Systems

Domain-specific discrete event modelling and simulation using graph transformation

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