Managing software complexity of adaptive systems

Roo, A.J. de

doi:10.3990/1.9789036533195

Cited by 3 publications

(4 citation statements)

References 78 publications

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“…It does not represent how the values of certain variables are derived from the values of other variables. For this purpose, a derivation graph is created (de Roo, 2012). The variable nodes that provide the input for the derivation are those variable nodes that match with the component's in-ports.…”

Section: Deriving a Mathematical Representationmentioning

confidence: 99%

MOO: An architectural framework for runtime optimization of multiple system objectives in embedded control software

Roo

Sözer

Bergmans

et al. 2013

Journal of Systems and Software

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Section: Deriving a Mathematical Representationmentioning

confidence: 99%

MOO: An architectural framework for runtime optimization of multiple system objectives in embedded control software

Roo

Sözer

Bergmans

et al. 2013

Journal of Systems and Software

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“…This means that the physical model instance executes the physical model to determine a new value of each physical variable, using the equations in the physical model and the values of the physical variables that have been changed. To perform this operation, the phys- For details about these algorithms, we refer the reader to [47]. In this paper, we will suffice with a discussion of the differences between the solving algorithm used by the physical model instance and the solving algorithms implemented in 20-Sim and Matlab Simulink.…”

Section: Executing Physical Modelsmentioning

confidence: 99%

“…Then, we can check whether each variable that is requested as output is solvable (meaning that there is a way to compute its value). The algorithm to perform this operation is explained in detail in [47].…”

Section: Composition Analysismentioning

confidence: 99%

Composing domain-specific physical models with general-purpose software modules in embedded control software

2012

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.A considerable portion of software systems today are adopted in the embedded control domain. Embedded control software deals with controlling a physical system, and as such models of physical characteristics become part of the embedded control software. In current practices, usually general-purpose languages (GPL), such as C/C++ are used for embedded systems development. Although a GPL is suitable for expressing general-purpose computation, it falls short in expressing the models of physical characteristics as desired. This reduces not only the readability of the code but also hampers reuse due to the lack of dedicated abstractions and composition operators. Moreover, domain-specific static and dynamic checks may not be applied effectively. There exist domain-specific modeling languages (DSML) and tools to specify models of physical characteristics. Although they are commonly used for simulation and documentation of physical systems, they are often not used to implement embedded control software. This is due to the fact that these DSMLs are not suitable to express the general-purpose computation and they cannot be easily composed with other software modules that are implemented in GPL. This paper presents a novel approach to combine a DSML to model physical characteristics and a GPL to implement general-purpose computation. The composition filters model is used to compose models specified in the DSML with modules specified in the GPL at the abstraction level of both languages. As such, this approach combines the benefits of using a DSML to model physical characteristics with the freedom of a GPL to implement general-purpose computation. The approach is illustrated using two industrial case studies from the printing systems domain

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“…As this part of the approach is not the main focus of this paper, we introduce here only the basics that are necessary to follow the next sections. More detail about how physical models are separated, composed and executed at runtime can be found in [25].…”

Section: Separation and Composition Of Physical Modelsmentioning

confidence: 99%

Verification and analysis of domain-specific models of physical characteristics in embedded control software

Roo

Sözer

Akşit

2012

Information and Software Technology

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Managing software complexity of adaptive systems

Cited by 3 publications

References 78 publications

MOO: An architectural framework for runtime optimization of multiple system objectives in embedded control software

MOO: An architectural framework for runtime optimization of multiple system objectives in embedded control software

Composing domain-specific physical models with general-purpose software modules in embedded control software

Verification and analysis of domain-specific models of physical characteristics in embedded control software

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