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Journal articles on the topic 'Model-driven engineering'

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Published: 4 June 2021
Last updated: 19 October 2024

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1

Rugaber, S., and K. Stirewalt. "Model-driven reverse engineering." IEEE Software 21, no. 4 (July 2004): 45–53. http://dx.doi.org/10.1109/ms.2004.23.

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2

Margaria, Tiziana, and Bernhard Steffen. "Continuous Model-Driven Engineering." Computer 42, no. 10 (October 2009): 106–9. http://dx.doi.org/10.1109/mc.2009.315.

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3

Kent, Stuart. "Model Driven Language Engineering." Electronic Notes in Theoretical Computer Science 72, no. 4 (March 2003): 6. http://dx.doi.org/10.1016/s1571-0661(04)80621-2.

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4

Maroukian, Krikor, Charalampos Apostolopoulos, and George Tsaramirsis. "Extending model driven engineering aspects to business engineering domain: a model driven business engineering approach." International Journal of Information Technology 9, no. 1 (February 22, 2017): 49–57. http://dx.doi.org/10.1007/s41870-017-0009-8.

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5

Schlereth, Michael, Marius Lauder, Sebastian Rose, and Andy SchÜrr. "Concurrent Model Driven Automation Engineering." atp edition - Automatisierungstechnische Praxis 52, no. 11 (November 1, 2010): 64. http://dx.doi.org/10.17560/atp.v52i11.421.

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6

Nambiar, Manoj, Ajay Kattepur, Gopal Bhaskaran, Rekha Singhal, and Subhasri Duttagupta. "Model Driven Software Performance Engineering." ACM SIGMETRICS Performance Evaluation Review 43, no. 4 (February 25, 2016): 53–62. http://dx.doi.org/10.1145/2897356.2897363.

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7

Jiménez-Pastor, Antonio, Antonio Garmendia, and Juan de Lara. "Scalable model exploration for model-driven engineering." Journal of Systems and Software 132 (October 2017): 204–25. http://dx.doi.org/10.1016/j.jss.2017.07.011.

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8

Zacharewicz, Gregory, Nicolas Daclin, Guy Doumeingts, and Hezam Haidar. "Model Driven Interoperability for System Engineering." Modelling 1, no. 2 (October 15, 2020): 94–121. http://dx.doi.org/10.3390/modelling1020007.

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To keep up to date, manufacturing enterprises need to use the latest results from the ICT sector, especially when collaborating with external partners in a supply chain and exchanging products and data. This has led to dealing with an increasing amount of heterogeneous information exchanged between partners including machines (physical means), humans and IT in the Supply Chain of ICT Systems (SC-ICTS). In this context, interoperability management is becoming more and more critical, but paradoxically, it is not yet fully efficiently anticipated, controlled and accompanied to recover from incompatibilities issues or failures. This paper intends to present how enterprise modeling, enterprise interoperability and model driven approaches can lead, together with system engineering architecture, to contribute to developing and improving the interoperability in the SC-ICTs. Model Driven System Engineering Architecture (MDSEA) is based on Enterprise Modeling using GRAI Model and its extensions. It gives enterprise internal developments guidelines, but originally, MDSEA is not the considering interoperability that is required between partners when setting a collaboration in the frame of SC-ICTS. As a result, the MDSEA, extended with interoperability concerns, led to the design of the MDISE (Model Driven Interoperability System Engineering) framework, which capitalizes on the research on enterprise interoperability. To finish, some proposals are made to extend the Model System Tool Box (MSTB) and the use of MDISE for Cyber Physical System (CPS) that are relevant components of SC-ICTS.
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9

Di Ruscio, Davide, Ludovico Iovino, and Alfonso Pierantonio. "Coupled Evolution in Model-Driven Engineering." IEEE Software 29, no. 6 (November 2012): 78–84. http://dx.doi.org/10.1109/ms.2012.153.

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10

Akiki, Pierre A., Arosha K. Bandara, and Yijun Yu. "Engineering Adaptive Model-Driven User Interfaces." IEEE Transactions on Software Engineering 42, no. 12 (December 1, 2016): 1118–47. http://dx.doi.org/10.1109/tse.2016.2553035.

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11

France, Robert, and Bernhard Rumpe. "Does model driven engineering tame complexity?" Software & Systems Modeling 6, no. 1 (January 25, 2007): 1–2. http://dx.doi.org/10.1007/s10270-006-0041-9.

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12

Brambilla, Marco, Jordi Cabot, and Manuel Wimmer. "Model-Driven Software Engineering in Practice." Synthesis Lectures on Software Engineering 1, no. 1 (September 21, 2012): 1–182. http://dx.doi.org/10.2200/s00441ed1v01y201208swe001.

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13

Schmidt, D. C. "Guest Editor's Introduction: Model-Driven Engineering." Computer 39, no. 2 (February 2006): 25–31. http://dx.doi.org/10.1109/mc.2006.58.

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14

Campos-López, Rubén, Esther Guerra, Juan de Lara, Alessandro Colantoni, and Antonio Garmendia. "Model-Driven Engineering for Augmented Reality." Journal of Object Technology 22, no. 2 (2023): 2:1. http://dx.doi.org/10.5381/jot.2023.22.2.a7.

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15

Jacome, Santiago, and Juan De Lara. "Controlling Meta-Model Extensibility in Model-Driven Engineering." IEEE Access 6 (2018): 19923–39. http://dx.doi.org/10.1109/access.2018.2821111.

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16

Stennikov, V. A., E. A. Barakhtenko, and D. V. Sokolov. "Model-Driven Engineering in the Software for Parameter Optimization of Heat Supply Systems." PROGRAMMNAYA INGENERIA 7, no. 2 (February 2016): 108–16. http://dx.doi.org/10.17587/prin.7.108-116.

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17

Götz, Sebastian, Andreas Fehn, Frank Rohde, and Thomas Kühn. "Model-driven Software Engineering for Construction Engineering: Quo Vadis?" Journal of Object Technology 19, no. 2 (2020): 2:1. http://dx.doi.org/10.5381/jot.2020.19.2.a2.

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18

Ma, Kun, Hao Teng, Lixin Du, and Kun Zhang. "Exploring model-driven engineering method for teaching software engineering." International Journal of Continuing Engineering Education and Life-Long Learning 26, no. 3 (2016): 294. http://dx.doi.org/10.1504/ijceell.2016.078448.

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19

Gaševic, Dragan, and Marek Hatala. "Model-Driven Engineering of Service-Oriented Systems." International Journal of Service Science, Management, Engineering, and Technology 1, no. 1 (January 2010): 17–32. http://dx.doi.org/10.4018/jssmet.2010010102.

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Service-oriented architectures (SOA) are an essential platform to provide infrastructures that support widespread collaboration between organizations. These service-oriented systems are a new context for software developers, who must now be equipped with new development methods and technologies. This new context has specific requirements, such as better collaboration and communication between business users and software engineering across organizations and increased agility of the development and maintenance processes to better respond to newly emerged or changed requirements. In this paper, the authors present a research agenda that looks at the use of a novel software engineering discipline—model-driven engineering. By switching the focus from low-level technical details to high-level problem-specific details, model-driven engineering addresses challenges in the development of service-oriented systems. This paper particularly discusses the approach to the development of service-oriented systems based on business process modeling, which integrate business vocabularies and rules in different stages of the development lifecycle. Here, model-driven engineering can provide many promising solutions.
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20

Cottenier, Thomas, Aswin van den Berg, and Tzilla Elrad. "Motorola WEAVR: Aspect and model-Driven Engineering." Journal of Object Technology 6, no. 7 (2007): 51. http://dx.doi.org/10.5381/jot.2007.6.7.a3.

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21

Jézéquel, Jean-Marc. "Model-Driven Engineering for Software Product Lines." ISRN Software Engineering 2012 (December 18, 2012): 1–24. http://dx.doi.org/10.5402/2012/670803.

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Modeling variability in the context of software product-lines has been around for about 25 years in the research community. It started with Feature Modeling and soon enough was extended to handle many different concerns. Beyond being used for a mere description and documentation of variability, variability models are more and more leveraged to produce other artifacts, such as configurators, code, or test cases. This paper overviews several classification dimensions of variability modeling and explores how do they fit with such artifact production purposes.
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22

Favre, Liliana Maria. "Formal Metamodeling for Secure Model-Driven Engineering." International Journal of Systems and Software Security and Protection 12, no. 2 (July 2021): 46–67. http://dx.doi.org/10.4018/ijsssp.2021070104.

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Systems and applications aligned with new paradigms such as cloud computing and internet of the things are becoming more complex and interconnected, expanding the areas in which they are susceptible to attacks. Their security can be addressed by using model-driven engineering (MDE). In this context, specific IoT or cloud computing metamodels emerged to support the systematic development of software. In general, they are specified through semiformal metamodels in MOF style. This article shows the theoretical foundations of a method for automatically constructing secure metamodels in the context of realizations of MDE such as MDA. The formal metamodeling language Nereus and systems of transformation rules to bridge the gap between formal specifications and MOF are described. The main contribution of this article is the definition of a system of transformation rules called NEREUStoMOF for transforming automatically formal metamodeling specifications in Nereus to semiformal-MOF metamodels annotated in OCL.
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23

Kim, Haeng-Kon. "Model Driven Engineering for Crop Monitoring Applications." International Journal of Software Engineering and Its Applications 10, no. 6 (June 30, 2016): 125–40. http://dx.doi.org/10.14257/ijseia.2016.10.6.11.

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24

Lara, Juan De, and Esther Guerra. "A Posteriori Typing for Model-Driven Engineering." ACM Transactions on Software Engineering and Methodology 25, no. 4 (May 5, 2017): 1–60. http://dx.doi.org/10.1145/3063384.

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25

Zhu, Zhi, Yonglin Lei, and Yifan Zhu. "Model Driven Combat Effectiveness Simulation Systems Engineering." Defence Science Journal 70, no. 1 (February 10, 2020): 54–59. http://dx.doi.org/10.14429/dsj.70.12777.

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Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors.
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26

Soukaina, Moujtahid. "Model Driven Engineering (MDE) Tools: A Survey." American Journal of Science, Engineering and Technology 3, no. 2 (2018): 29. http://dx.doi.org/10.11648/j.ajset.20180302.11.

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27

Langegger, Andreas, Jürgen Palkoska, and Roland Wagner. "DaVinci ‐ A model‐driven web engineering framework." International Journal of Web Information Systems 2, no. 2 (May 2006): 119–34. http://dx.doi.org/10.1108/17440080680000106.

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28

Gomez, A., A. Boronat, J. A. Carsi, I. Ramos, C. Taubner, and S. Eckstein. "Biological Data Processing using Model Driven Engineering." IEEE Latin America Transactions 6, no. 4 (August 2008): 324–31. http://dx.doi.org/10.1109/tla.2008.4815285.

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29

Ceri, Stefano, Florian Daniel, Federico M. Facca, and Maristella Matera. "Model-driven Engineering of Active Context-awareness." World Wide Web 10, no. 4 (March 2, 2007): 387–413. http://dx.doi.org/10.1007/s11280-006-0014-5.

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30

Giraldo, Fáber D., Sergio España, Óscar Pastor, and William J. Giraldo. "Considerations about quality in model-driven engineering." Software Quality Journal 26, no. 2 (December 19, 2016): 685–750. http://dx.doi.org/10.1007/s11219-016-9350-6.

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31

Di Ruscio, Davide, Juan de Lara, and Alfonso Pierantonio. "Special issue on Flexible Model Driven Engineering." Computer Languages, Systems & Structures 49 (September 2017): 174–75. http://dx.doi.org/10.1016/j.cl.2016.12.003.

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32

Martinez, Salvador, Sebastien Gerard, and Jordi Cabot. "On Watermarking for Collaborative Model-Driven Engineering." IEEE Access 6 (2018): 29715–28. http://dx.doi.org/10.1109/access.2018.2841020.

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33

Gray, J., Yuehua Lin, and Jing Zhang. "Automating Change Evolution in Model-Driven Engineering." Computer 39, no. 2 (February 2006): 51–58. http://dx.doi.org/10.1109/mc.2006.45.

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34

Jouault, Frédéric, Jean Bézivin, and Mikaël Barbero. "Towards an advanced model-driven engineering toolbox." Innovations in Systems and Software Engineering 5, no. 1 (March 2009): 5–12. http://dx.doi.org/10.1007/s11334-009-0082-7.

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35

Ciancone, Andrea, Antonio Filieri, and Raffaela Mirandola. "Testing operational transformations in model-driven engineering." Innovations in Systems and Software Engineering 10, no. 1 (May 1, 2013): 19–32. http://dx.doi.org/10.1007/s11334-013-0208-9.

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36

Her, Jin Sun, Hao Yuan, and Soo Dong Kim. "Traceability-centric model-driven object-oriented engineering." Information and Software Technology 52, no. 8 (August 2010): 845–70. http://dx.doi.org/10.1016/j.infsof.2010.03.012.

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37

Brunelière, Hugo, Jordi Cabot, Grégoire Dupé, and Frédéric Madiot. "MoDisco: A model driven reverse engineering framework." Information and Software Technology 56, no. 8 (August 2014): 1012–32. http://dx.doi.org/10.1016/j.infsof.2014.04.007.

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38

Barbier, Guillaume, Véronique Cucchi, and David R. C. Hill. "Model-driven engineering applied to crop modeling." Ecological Informatics 26 (March 2015): 173–81. http://dx.doi.org/10.1016/j.ecoinf.2014.05.004.

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39

Grace, Paul, Brian Pickering, and Mike Surridge. "Model-driven interoperability: engineering heterogeneous IoT systems." Annals of Telecommunications 71, no. 3-4 (November 25, 2015): 141–50. http://dx.doi.org/10.1007/s12243-015-0487-2.

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40

Davies, Jim, Charles Crichton, Edward Crichton, David Neilson, and Ib Holm Sørensen. "Formality, Evolution, and Model-driven Software Engineering." Electronic Notes in Theoretical Computer Science 130 (May 2005): 39–55. http://dx.doi.org/10.1016/j.entcs.2005.03.004.

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41

Korevaar, Peter A., Christophe Grenier, and E. W. Meijer. "Toward model-driven engineering of supramolecular copolymers." Journal of Polymer Science Part A: Polymer Chemistry 53, no. 2 (October 29, 2014): 385–91. http://dx.doi.org/10.1002/pola.27446.

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42

Rivero, José Matías, Julián Grigera, Gustavo Rossi, Esteban Robles Luna, Francisco Montero, and Martin Gaedke. "Mockup-Driven Development: Providing agile support for Model-Driven Web Engineering." Information and Software Technology 56, no. 6 (June 2014): 670–87. http://dx.doi.org/10.1016/j.infsof.2014.01.011.

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43

Rahad, Khandoker, Omar Badreddin, and Sayed Mohsin Reza. "The human in model‐driven engineering loop: A case study on integrating handwritten code in model‐driven engineering repositories." Software: Practice and Experience 51, no. 6 (February 18, 2021): 1308–21. http://dx.doi.org/10.1002/spe.2957.

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44

Lütjen, Michael, Hans-Jörg Kreowski, Marco Franke, Klaus-Dieter Thoben, and Michael Freitag. "Model-driven Logistics Engineering – Challenges of Model and Object Transformation." Procedia Technology 15 (2014): 303–12. http://dx.doi.org/10.1016/j.protcy.2014.09.084.

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45

Abouzahra, Anas, Ayoub Sabraoui, and Karim Afdel. "Model composition in Model Driven Engineering: A systematic literature review." Information and Software Technology 125 (September 2020): 106316. http://dx.doi.org/10.1016/j.infsof.2020.106316.

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46

Krstic, Srdan, Hoang Nguyen, and David Basin. "Model-driven Privacy." Proceedings on Privacy Enhancing Technologies 2024, no. 1 (January 2024): 314–29. http://dx.doi.org/10.56553/popets-2024-0018.

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Data protection regulations in many countries require IT systems to implement baseline privacy requirements like purpose limitation and consent as mandated by the GDPR. Such requirements are often specified in the system’s privacy policy and are challenging to implement as system developers must address them consistently and in a cross-cutting manner. Moreover, without a formal connection between a system’s privacy policy and its implementation, the system’s correctness and evolution are extremely difficult to attain. We propose a model-driven development methodology that incorporates privacy policies into the system design. Namely, we define a system’s privacy model, which has precise semantics and is used to specify privacy policies. We provide semantic-preserving model transformations that generate system implementations that enforce the given privacy policies by design. We implement two such model transformations, targeting C# and Python system implementations. We evaluate our methodology on three substantial case studies and show the enforcement of privacy policies related to purpose limitation and consent. Our evaluation also demonstrates our approach’s generality, effectiveness, and modest overhead.
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47

Hincapié Londoño, Jesús Andrés, and Jhon Freddy Duitama. "Model-driven web engineering methods: a literature review." Revista Facultad de Ingeniería Universidad de Antioquia, no. 63 (August 1, 2012): 69–81. http://dx.doi.org/10.17533/udea.redin.12487.

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This paper presents some of the model-driven Web engineering methods that have been proposed, and discusses and analyzes the advantages and disadvantages of such methods regarding current tendencies and best practices on model-driven engineering. The idea is to present each approach and analyze the models they propose to represent Web applications, the architectural aspects in the transformations, and the use of current Web user interface technologies in the generated code. This is done in order to depict possible research lines for future works on the model-driven Web engineering area.
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48

de Araújo Silva, Edson, Eduardo Valentin, Jose Reginaldo Hughes Carvalho, and Raimundo da Silva Barreto. "A survey of Model Driven Engineering in robotics." Journal of Computer Languages 62 (February 2021): 101021. http://dx.doi.org/10.1016/j.cola.2020.101021.

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49

Fatolahi, Ali, and Stephane S. Some. "Assessing a Model-Driven Web-Application Engineering Approach." Journal of Software Engineering and Applications 07, no. 05 (2014): 360–70. http://dx.doi.org/10.4236/jsea.2014.75033.

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50

M'barek, Salwa, Leila Baccouche, and Henda Ben Ghezala. "Model Driven Engineering for Quality of Service Management." Journal of Database Management 27, no. 4 (October 2016): 24–38. http://dx.doi.org/10.4018/jdm.2016100102.

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Real-time applications managing a large number of real-time data require the use of Real-time Database Management Systems (RTDBMS) to meet temporal constraints of both real-time data and transactions. However, a RTDBMS has a dynamic workload and may be frequently overloaded since the arrival times and workloads of user transactions are unpredictable. Therefore, Quality of Service management solutions have been proposed to guarantee the stability of RTDBMS even during unpredictable overload periods. While effective, the design and reuse of these solutions is challenging because they are not formally modeled and there is no tool neither a methodology that helps us design such solutions. To address these issues, the authors propose a design framework based on the Model-Driven Engineering approach providing a modeling architecture, a strategic methodology and a software tool to support modeling and reusing such solutions. The framework is implemented and tested for a real Qos management solution.
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