A classification of modeling variability in simulink

Weiland, Jens; Manhart, Peter

doi:10.1145/2556624.2556632

Cited by 18 publications

(12 citation statements)

References 5 publications

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“…2 and 3) connect features from all the feature models, except Haz-ard_FM, with the configuration parameters (representing variability) in the underlying Simulink model. Modeling variability in Simulink models is studied in the literature (e.g., in [21]) and also implemented in tools, e.g., pure::variants. From our industrial partner, we already have some Simulink models that have parameters that can be seen as configurations points (as the sensors' tolerance and the atmospheric pressure in the running case study).…”

Section: Modeling Solutionmentioning

confidence: 99%

Towards a Framework for the Analysis of Multi-Product Lines in the Automotive Domain

Ali

Arcaini

Hasuo

et al. 2019

Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems

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Safety analyses in the automotive domain (in particular automated driving) present unprecedented challenges due to its complexity and tight integration with the physical environment. Given the diversity in the types of cars, potentially unlimited number of possible environmental and driving conditions, it is crucial to devise a systematic way of managing variability in hazards, driving and environmental conditions in individual cars, families of cars, and families of families of cars to facilitate analyses efficiently. To this end, we present our ongoing work in a research project that focuses on devising a model-based reasoning framework for systematically managing hazards in the automotive domain and supporting safety analyses (e.g., falsification). CCS CONCEPTS • Computing methodologies → Model verification and validation; • Software and its engineering → Software product lines;

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Section: Modeling Solutionmentioning

confidence: 99%

Towards a Framework for the Analysis of Multi-Product Lines in the Automotive Domain

Ali

Arcaini

Hasuo

et al. 2019

Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems

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“…Simulink is a popular block diagram language with some built-in support for handling variability (Weiland and Manhart 2013). It supports conditional blocks which are executed conditionally, and model variant blocks which conditionally select a block from a set of variants.…”

Section: Variability Support For Diagram Languagesmentioning

confidence: 99%

Bloqqi: modular feature-based block diagram programming

Fors

Hedin

2016

Proceedings of the 2016 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

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Automation programming is typically done using blocks and dataflow connections, in diagram languages that support userdefined block types. Often, these types are intended to be instantiated and connected to other blocks in common patterns, corresponding to anticipated variability. We present the new language mechanisms of wirings and recommendations that allow these intentions to be encoded as features in libraries. A wiring describes how a given block is typically connected to other blocks, and a recommendation describes where such a wiring is typically applied as a feature. This allows feature-based wizards to be generated for user-defined libraries, making it easy to construct applications that make use of the encoded patterns.

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“…DM is also already applied to embedded software systems. For instance, state-of-the-art research focuses on MATLAB/Simulink models in the automotive domain [37,38]. Research on variability modeling for aPS is still in an early stage.…”

Section: B Variability Modelingmentioning

confidence: 99%

“…3b. Examples on how DM is applied for behavioral models are given, e.g., in [37,38] for MATLAB/Simulink models and in [33] for UML state charts. These findings can be applied for further behavioral models, e.g., arbitrary state machines as necessary for our stream processing functions.…”

Section: B the Delta Model Extensionmentioning

confidence: 99%

Towards interdisciplinary variability modeling for automated production systems: Opportunities and challenges when applying delta modeling: A case study

Vogel‐Heuser

Mund

Kowal

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Automated production systems involve multiple engineering disciplines and often operate for several decades. Therefore, in order to leverage benefits of model-based system engineering, modeling approaches must handle both multiple disciplines and variability. As a first step towards variability modeling and management, a small case study is carried out to investigate the opportunities and challenges when enhancing an interdisciplinary modeling approach with delta modeling -a technique for specifying variants and versions. The paper reflects critically its applicability, implications as well as potential beneficial impacts and discusses encouraging challenges and next steps towards an interdisciplinary approach for variability modeling and management.

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A classification of modeling variability in simulink

Cited by 18 publications

References 5 publications

Towards a Framework for the Analysis of Multi-Product Lines in the Automotive Domain

Towards a Framework for the Analysis of Multi-Product Lines in the Automotive Domain

Bloqqi: modular feature-based block diagram programming

Towards interdisciplinary variability modeling for automated production systems: Opportunities and challenges when applying delta modeling: A case study

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