A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

Combemale, Benôıt; Kienzle, Jörg; Mussbacher, Gunter; Ali, Hyacinth; Amyot, Daniel; Bagherzadeh, Mojtaba; Batot, Edouard; Bencomo, Nelly; Benni, Benjamin; Bruel, Jean‐Michel; Cabot, Jordi; Cheng, Betty H. C.; Collet, Philippe; Engels, Gregor; Heinrich, Robert; Jézéquel, Jean-Marc; Koziolek, Anne; Mosser, Sébastien; Reussner, Ralf; Sahraoui, Houari; Saini, Rijul; Sallou, June; Stinckwich, Serge; Syriani, Eugene; Wimmer, Manuel

doi:10.1109/ms.2020.2995125

Cited by 30 publications

(29 citation statements)

References 13 publications

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“…At the same time, the availability of data that can be observed from different modeling activities has increased significantly, leading to many opportunities to provide intelligent modeling assistance to modelers, e.g., using previous modeling experiences or historical information in model repositories, and considering the modelers' context or domain-specific knowledge. Data-driven techniques [15] (e.g., data mining and machine learning [20]) enable the automatic derivation of modeling knowledge and the provision of contextaware assistance. It is of utmost importance to make use of this data and associated techniques, combined with the power of abstraction, to assist modelers in their modeling activities.…”

Section: Introductionmentioning

confidence: 99%

Towards an assessment grid for intelligent modeling assistance

Mussbacher

Combemale²,

Abrahão

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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The ever-growing complexity of systems, the growing number of stakeholders, and the corresponding continuous emergence of new domain-specific modeling abstractions has led to significantly higher cognitive load on modelers. There is an urgent need to provide modelers with better, more Intelligent Modeling Assistants (IMAs). An important factor to consider is the ability to assess and compare, to learn from existing and inform future IMAs, while potentially combining them. Recently, a conceptual Reference Framework for Intelligent Modeling Assistance (RF-IMA) was proposed. RF-IMA defines the main required components and high-level properties of IMAs. In this paper, we present a detailed, level-wise definition for the properties of RF-IMA to enable a better understanding, comparison, and selection of existing and future IMAs. The proposed levels are a first step towards a comprehensive assessment grid for intelligent modeling assistance. For an initial validation of the proposed levels, we assess the existing landscape of intelligent modeling assistance and three future scenarios of intelligent modeling assistance against these levels.

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Section: Introductionmentioning

confidence: 99%

Towards an assessment grid for intelligent modeling assistance

Mussbacher

Combemale²,

Abrahão

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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“…-Developing and evolving adaption methods for SCPS where the goal models exploit important quantitative information such as contribution weights, importance levels to stakeholders, and indicators that measure different facets of the context. Such information is often necessary in models for data-centric systems and is very important for non-trivial adaptive SCPSs [113]. Despite many observed gaps and challenges, we believe the benefits of goal modeling (potential or actual) combined with SysML for adaptive SCPSs outweigh the identified drawbacks, and that further research will bring innovative and practical solutions in the near future.…”

Section: Discussionmentioning

confidence: 98%

Self-Adaptation Driven by SysML and Goal Models -- A Literature Review

Anda¹,

Amyot²

2022

EISEJ

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Background: Socio-cyber-physical systems (SCPSs) are a type of cyber-physical systems with social concerns. Many SCPSs, such as smart homes, must be able to adapt to reach an optimal symbiosis with users and their contexts. The Systems Modeling Language (SysML) is frequently used to specify ordinary CPSs, whereas goal modeling is a requirements engineering approach used to describe and reason about social concerns. Objective: This paper aims to assess existing modeling techniques that support adaptation in SCPSs, and in particular those that integrate SysML with goal modeling. Method: A systematic literature review presents the main contributions of 52 English articles selected from five databases that use both SysML and goal models (17 techniques), SysML models only (11 techniques), or goal models only (8 techniques) for analysis and self-adaptation. Result: Existing techniques have provided increasingly better modeling support for adaptation in a SCPS context, but overall analysis support remains weak. The techniques that combine SysML and goal modeling offer interesting benefits by tracing goals to SysML (requirements) diagrams and influencing the generation of predefined adaptation strategies for expected contexts, but few target adaptation explicitly and most still suffer from a partial coverage of important goal modeling concepts and of traceability management issues.

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“…Combemale et al [18] proposed the conceptual models and data (MODA) framework. They aim at providing a reference for model-driven and data-driven modelling issues.…”

Section: E Integrating a Priori Models With Models Learnt From Datamentioning

confidence: 99%