A survey and comparison of transformation tools based on the transformation tool contest

Jakumeit, Edgar; Buchwald, Sebastian; Wagelaar, Dennis; Li, Dan; Hegedüs, Ábel; Herrmannsdörfer, Markus; Horn, Tassilo; Kalnina, Elina; Krause, Christian; Lano, Kevin; Lepper, Markus; Rensink, Arend; Rose, Louis M.; Wätzoldt, Sebastian; Mazanek, Steffen

doi:10.1016/j.scico.2013.10.009

Cited by 36 publications

(21 citation statements)

References 25 publications

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“…The comprehensively capabilities of collaboration specifications between independent systems inside the SoS are introduced in Section 5. 5. Due to the focus on adaptive behavior, the modeling of reconfiguration, adaptation, and meta-adaptation in Deurema are described in Section 5.…”

Section: Deurema Modeling Languagementioning

confidence: 99%

“…Of course, the type of the reference between objects must be specified in a graph transformation rule pattern modeled in Deurema as introduced in Section 2.2. 5.…”

Section: Behavior Module Template Examplementioning

confidence: 99%

“…For illustrating the delegation of the reflected information, an excerpt of the Self-Configuring feedback loop module is modeled in the template notation on the right. 5 As shown, the reflect module operation is delegated to the Architecture runtime model. Deurema ensures that every performed read model operation on the runtime model retrieves up-to-date, reflected knowledge from the architecture of the smart car system.…”

Section: Deurema Reflection Reconfiguration and Adaptationmentioning

confidence: 99%

“…This is represented by the postactive state as discussed above and explicitly considered in the execution state model in Figure 7. 5. The coupling of the two states of the module and the corresponding interaction is reflected in the example simulation rule in Figure 7 The inference engine searches for an interaction, where a corresponding module is the player of the mapped collaboration role.…”

Section: State: Collaboratingmentioning

confidence: 99%

“…For white boxes, the trigger and action are combined in a graph transformation rule, which can be directly applied by the interpreter. During the execution of the adaptation effect, the Deurema execution environment recognizes changes on the underlying runtime models depicted as modifying runtime model operation in step (5). Therefore, modifying model operations (e. g., write or annotate in step (5)) as well as affecting module operations in step (6) can be applied by the Deurema interpreter accordingly.…”

Section: Execution Semantic Behavior Modelmentioning

confidence: 99%

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Modeling Collaborations in Adaptive Systems of Systems

Wätzoldt

Giese

2015

Proceedings of the 2015 European Conference on Software Architecture Workshops

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Recently, due to an increasing demand on functionality and flexibility, beforehand isolated systems have become interconnected to gain powerful adaptive System of Systems (SoS) solutions with an overall robust, flexible and emergent behavior. The adaptive SoS comprises a variety of different system types ranging from small embedded to adaptive cyber-physical systems. On the one hand, each system is independent, follows a local strategy and optimizes its behavior to reach its goals. On the other hand, systems must cooperate with each other to enrich the overall functionality to jointly perform on the SoS level reaching global goals, which cannot be satisfied by one system alone. Due to difficulties of local and global behavior optimizations conflicts may arise between systems that have to be solved by the adaptive SoS.This thesis proposes a modeling language that facilitates the description of an adaptive SoS by considering the adaptation capabilities in form of feedback loops as first class entities. Moreover, this thesis adopts the Models@runtime approach to integrate the available knowledge in the systems as runtime models into the modeled adaptation logic. Furthermore, the modeling language focuses on the description of system interactions within the adaptive SoS to reason about individual system functionality and how it emerges via collaborations to an overall joint SoS behavior. Therefore, the modeling language approach enables the specification of local adaptive system behavior, the integration of knowledge in form of runtime models and the joint interactions via collaboration to place the available adaptive behavior in an overall layered, adaptive SoS architecture.Beside the modeling language, this thesis proposes analysis rules to investigate the modeled adaptive SoS, which enables the detection of architectural patterns as well as design flaws and pinpoints to possible system threats. Moreover, a simulation framework is presented, which allows the direct execution of the modeled SoS architecture. Therefore, the analysis rules and the simulation framework can be used to verify the interplay between systems as well as the modeled adaptation effects within the SoS. This thesis realizes the proposed concepts of the modeling language by mapping them to a state of the art standard from the automotive domain and thus, showing their applicability to actual systems. Finally, the modeling language approach is evaluated by remodeling up to date research scenarios from different domains, which demonstrates that the modeling language concepts are powerful enough to cope with a broad range of existing research problems.-I - Zusammenfassung

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Section: Deurema Modeling Languagementioning

confidence: 99%

“…Of course, the type of the reference between objects must be specified in a graph transformation rule pattern modeled in Deurema as introduced in Section 2.2. 5.…”

Section: Behavior Module Template Examplementioning

confidence: 99%

Section: Deurema Reflection Reconfiguration and Adaptationmentioning

confidence: 99%

Section: State: Collaboratingmentioning

confidence: 99%

Section: Execution Semantic Behavior Modelmentioning

confidence: 99%

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Modeling Collaborations in Adaptive Systems of Systems

Wätzoldt

Giese

2015

Proceedings of the 2015 European Conference on Software Architecture Workshops

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Metric Temporal Graph Logic over Typed Attributed Graphs

Giese

Maximova

Sakizloglou

et al. 2019

Fundamental Approaches to Software Engineering

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Various kinds of typed attributed graphs can be used to represent states of systems from a broad range of domains. For dynamic systems, established formalisms such as graph transformation can provide a formal model for defining state sequences. We consider the case where time may elapse between state changes and introduce a logic, called Metric Temporal Graph Logic (MTGL), to reason about such timed graph sequences. With this logic, we express properties on the structure and attributes of states as well as on the occurrence of states over time that are related by their inner structure, which no formal logic over graphs concisely accomplishes so far. Firstly, based on timed graph sequences as models for system evolution, we define MTGL by integrating the temporal operator until with time bounds into the well-established logic of (nested) graph conditions. Secondly, we outline how a finite timed graph sequence can be represented as a single graph containing all changes over time (called graph with history), how the satisfaction of MTGL conditions can be defined for such a graph and show that both representations satisfy the same MTGL conditions. Thirdly, we present how MTGL conditions can be reduced to (nested) graph conditions and show using this reduction that both underlying logics are equally expressive. Finally, we present an extension of the tool AutoGraph allowing to check the satisfaction of MTGL conditions for timed graph sequences, by checking the satisfaction of the (nested) graph conditions, obtained using the proposed reduction, for the graph with history corresponding to the timed graph sequence.

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Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems

Magnien,

Cecchetti,

Ruscelli

et al. 2022

Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification

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Railway Traffic Management Systems (TMSs) handle data from multiple railway subsystems, including Rail Business Services (such as interlocking, RBC, maintenance service, etc.) and external services (such as passenger information systems, weather forecast, etc.). In turn, the data from these subsystems are described in several models or ontologies contributed by various organizations or projects which are in a process of converging or federation. The challenge of the Shift2Rail OPTIMA project, which is implementing a communication platform for virtual testing of new applications for railway TMS, is to allow the exchange of data between different services or users and to support new traffic management applications, enabling access to a large number of disparate data sources. In this paper, the core activities of the OPTIMA project related to the formulation and standardization of a common data model are described. A new Common Data Model is developed based on standardized data structures to enable the seamless exchange of large amounts of data between different and heterogeneous sources and consumers of data, that contributes to the building of next generation of a more effective and efficient railway TMS suitable to offer precise and real-time traffic information to railway operators and other end users.

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A survey and comparison of transformation tools based on the transformation tool contest

Cited by 36 publications

References 25 publications

Modeling Collaborations in Adaptive Systems of Systems

Modeling Collaborations in Adaptive Systems of Systems

Metric Temporal Graph Logic over Typed Attributed Graphs

Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems

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