Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime

Szvetits, Michael; Zdun, Uwe

doi:10.1007/s10270-013-0394-9

Cited by 85 publications

(55 citation statements)

References 253 publications

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“…"The big money (of Industry 4.0) is on two things; zero unscheduled downtime and resource efficiency" -Bill Ruh, vice president of GE Software [4] Model-based systems engineering (MBSE) is a key enabler for building complex systems of systems as can be seen by the increased number of related publications in key conferences and journals [5,6,7]. For successfully engineering Industry 4.0 systems of systems, the MBSE community plays a crucial role by enabling the aforementioned design principles.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Mapping Study on Modeling for Industry 4.0

Wortmann

Combemale

Barais

2017

2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Industry 4.0 is a vision of manufacturing in which smart, interconnected production systems optimize the complete value-added chain to reduce cost and time-to-market. At the core of Industry 4.0 is the smart factory of the future, whose successful deployment requires solving challenges from many domains. Model-based systems engineering (MBSE) is a key enabler for such complex systems of systems as can be seen by the increased number of related publications in key conferences and journals. This paper aims to characterize the state of the art of MBSE for the smart factory through a systematic mapping study on this topic. Adopting a detailed search strategy, 1466 papers were initially identified. Of these, 222 papers were selected and categorized using a particular classification scheme. Hence, we present the concerns addressed by the modeling community for Industry 4.0, how these are investigated, where these are published, and by whom. The resulting research landscape can help to understand, guide, and compare research in this field. In particular, this report identifies the Industry 4.0 challenges addressed by the modeling community, but also the challenges that seem to be less investigated.

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Section: Introductionmentioning

confidence: 99%

A Systematic Mapping Study on Modeling for Industry 4.0

Wortmann

Combemale

Barais

2017

2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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“…In the latter case, the context instance corresponds to the current context, that is, the context surrounding a running system. In such latter case, we assume a requirements model at runtime [25] that facilitates runtime adaptation, such as the goal-based service composition approach in [3]. The advances in sensor and context-aware technologies enable the capture of such context instances and various methods for capturing this (implicit) context are considerably discussed in the literature.…”

Section: Requirements Variability In Varying Contexts and Preferencesmentioning

confidence: 99%

Specifying and reasoning about contextual preferences in the goal-oriented requirements modelling

Botangen

Yongchareon

et al. 2018

Proceedings of the Australasian Computer Science Week Multiconference

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Goal-oriented requirements variability modelling has established the understanding for adaptability in the early stage of software development -the Requirements Engineering phase. Goaloriented requirements variability modelling considers both the intentions, which are captured as goals in goal models, and the preferences of different stakeholders as the main sources of system behaviour variability. Most often, however, intentions and preferences vary according to contexts. In this paper, we propose an approach for a contextual preference-based requirements variability analysis in the goal-oriented Requirements Engineering. We introduce a quantitative contextual preference specification to express the varying preferences imposed over requirements that are represented in the goal model. Such contextual preferences are used as criteria to evaluate alternative solutions that satisfy the requirements variability problem. We utilise a state-of-the-art reasoning implementation from the Answer Set Programming domain to automate the derivation and evaluation of solutions that fulfill the goals and satisfy the contextual preferences. Our approach will support systems analysts in their decisions upon alternative design solutions that define subsequent system implementations. 1 CCS CONCEPTS• Software and its engineering ~ Requirements analysis • Software and its engineering ~ Software design engineering

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“…Firstly, the review conducted by Svetits and Zdun [9] presented a comprehensive research on models at runtime literature, classifying the articles in terms of: objectives, architectures, techniques, and kinds of models. Secondly, the SLR conducted by Giachetti et al [16] focused on interoperability in MDD processes, by evaluating five related features from selected approaches.…”

Section: A Related Workmentioning

confidence: 99%

“…The "model-driven" approaches based on runtime models raise the importance of modeling activities. As identified by [9] [10] and several others, both topics present important problems to be addressed, which increases the relevancy of research in this field.…”

Section: Introductionmentioning

confidence: 99%

“…Conceptually, a runtime model is defined as an abstraction of a running system that is being manipulated during its execution for a specific purpose. Runtime models are also a causally connected selfrepresentation of the system that emphasizes the structure, behavior or goals from a problem space perspective [9]. Therefore, the combination of runtime models and selfadaptive systems opens the possibility of using abstractions at different levels to change the behavior of running systems.…”

Section: Introductionmentioning

confidence: 99%

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Self-adaptive Systems Driven by Runtime Models

Thiry¹,

Schmidt²

2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Model-Driven Software Engineering (MDSE) represents a promising research area with a variety of challenging issues open for discussion. Expanding the limits of the MDSE paradigm, runtime models keep abstract representations of the running system in order to trigger on-the-fly software reconfigurations. One of the most popular applications of runtime models are self-adaptive systems, since abstractions can be finetuned not only in the development phases, but also in runtime. As this kind of system needs to modify its behavior during execution, this can be achieved by means of high-level model interventions. The objective of this article is to present relevant approaches of self-adaptive systems driven by runtime models. This article can help practitioners to get an overall picture of current approaches, in terms of methods, techniques and tools. Researchers can also be inspired to create new or to extend current approaches, facing the challenges identified here. To that end, we conducted a rigorous Systematic Literature Review based on the guidelines proposed by Kitchenham. This paper provides answers for four research questions, based on 16 selected articles. In the conclusion, we present some considerations and challenges based on the results obtained from this review.

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Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime

Cited by 85 publications

References 253 publications

A Systematic Mapping Study on Modeling for Industry 4.0

A Systematic Mapping Study on Modeling for Industry 4.0

Specifying and reasoning about contextual preferences in the goal-oriented requirements modelling

Self-adaptive Systems Driven by Runtime Models

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