MDE in Practice for Computational Science

Bruel, Jean‐Michel; Combemale, Benôıt; Ober, Iulian; Raynal, Hélène

doi:10.1016/j.procs.2015.05.182

Cited by 16 publications

(14 citation statements)

References 15 publications

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“…However, for the industrial context, specialized websites (grey literature 12 ) make significant contributions to the quality from a practitioners' perspective. We found 49 industrial MDE Quality evidence in industry MDE Quality evidence in academic & research Industrial issue 01 (Vara and Marcos, 2012;Cuadrado et al, 2014;Hutchinson et al, 2014;Bertrand Portier, 2009;Haan, 2008;Whittle et al, 2014;Dubray, 2011;Linders, 2015;Tone, 2010;Finnie, 2015;Klinke, 2008;Vallecillo, 2014;Hebig and Bendraou, 2014;Brown, 2009;DenHaan, 2009DenHaan, , 2011bPierson, 2007;Cabot, 2009;Brambilla, 2016;OMG, 2016;Mohagheghi et al, 2009b;Igarza et al, 2012;Clark and Muller, 2012;Whittle et al, 2015;Aranda et al, 2012;Baker et al, 2005;Whittle et al, 2013;Cuadrado et al, 2014;Hoang, 2012;Platania, 2016;Fournier, 2008) (Agner et al, 2013;Panach et al, 2015a;Davies et al, 2006;Mussbacher et al, 2014;Quintero and Muñoz, 2011;Quintero et al, 2012;Poruban et al, 2014;Bruel et al, 2015;…”

Section: Findings In the Literature Review Of Mismatchmentioning

confidence: 99%

Considerations about quality in model-driven engineering

et al. 2016

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Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. doi:10.1007/s11219-016-9350-6. Considerations about quality in model-driven engineeringCurrent state and challenges.Fáber D. Giraldo · Sergio España · Oscar Pastor · William J. GiraldoReceived: date / Accepted: date Abstract The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the modeldriven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modelling language level.In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives.We identified sixteen categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic.Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modelling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners.We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modelling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.

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Section: Findings In the Literature Review Of Mismatchmentioning

confidence: 99%

Considerations about quality in model-driven engineering

et al. 2016

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“…Recently, we conducted an empirical study to evaluate the effectiveness of using DSMLs to support the simulation of a water management system for use in a farming context [3]. DSMLs were effective in engaging the domain experts in modeling both topological and behavioral concerns.…”

Section: Modeling In Engineering and Sci-encesmentioning

confidence: 99%

“…Many of these decisions need to dynamically manage the respective sustainability systems. While progress has been made by leveraging modeling technology to manage the complexity of sustainability systems [6,3,14], numerous software engineering and modeling challenges remain. As the problem complexity, size, and scope increase, stakeholders and their needs change, and technological advances offer new options to exploit.…”

Section: Introductionmentioning

confidence: 99%

Modeling for sustainability

Combemale

Cheng

Moreira

et al. 2016

Proceedings of the 8th International Workshop on Modeling in Software Engineering

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. ABSTRACTVarious disciplines use models for different purposes. While engineering models, including software engineering models, are often developed to guide the construction of a nonexistent system, scientific models, in contrast, are created to better understand a natural phenomenon (i.e., an already existing system). An engineering model may incorporate scientific models to build a system. Both engineering and scientific models have been used to support sustainability, but largely in a loosely-coupled fashion, independently developed and maintained from each other. Due to the inherent complex nature of sustainability that must balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines. This paper offers a vision that synergistically combines engineering and scientific models to enable broader engagement of society for addressing sustainability concerns, informed decision-making based on moreaccessible scientific models and data, and automated feedback to the engineering models to support dynamic adaptation of sustainability systems. To support this vision, we identify a number of research challenges to be addressed with particular emphasis on the socio-technical benefits of modeling.

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“…Building a model of some real-world phenomenon seems to be an intrinsic human endeavor to understand and predict occurrences in the world [4]. If lots of approaches discuss the benefits of Model-Driven Engineering (MDE) approach to help experts in designing simulators, this paper proposes an industrial case study for representing industrial process and energy management constraints.…”

Section: Introductionmentioning

confidence: 99%

Efficient use of local energy

Rio

Maurel²,

Barais³

et al. 2018

Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

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Self-consumption of renewable energies is defined as electricity that is produced from renewable energy sources, not injected to the distribution or transmission grid or instantaneously withdrawn from the grid and consumed by the owner of the power production unit or by associates directly contracted to the producer. Designing solutions in favor of self-consumption for small industries or city districts is challenging. It consists in designing an energy production system made of solar panels, wind turbines, batteries that fit the annual weather prediction and the industrial or human activity. In this context, this paper reports the context of this business domain, its challenges, and the application of modeling that leads to a solution. Through this article, we highlight the essentials of a domain specific modeling language designed to let domain experts run their own simulations, we compare with existing practices that exist in such a company and we discuss the benefits and the limits of the use of modeling in such context.

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MDE in Practice for Computational Science

Cited by 16 publications

References 15 publications

Considerations about quality in model-driven engineering

Considerations about quality in model-driven engineering

Modeling for sustainability

Efficient use of local energy

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