11 research outputs found

    Virtual Composition of EMF Models

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    National audienceModel composition is a very important modeling task as it allows to combine various perspectives of a system (represented by various models) into a single specialized view (a composed model). Several approaches have been proposed to tackle this problem, but they present some important limitations concerning efficiency, interoperability, and/or synchronization issues (mainly due to the element cloning mechanism used to create the composed model). In this paper we propose a new model composition method based on the virtualization of the composition mechanism. In our approach, the composed model is in fact created as a virtual model that redirects all its model access and manipulation requests directly to the set of base models from which it was generated. This is done transparently for the designer. Our mechanism improves the composition process with relation to the limitations mentioned above. The solution has been implemented and validated in a prototype tool on top of EMF.La composition de modèles est une tâche de modélisation très importante car elle permet de combiner différents points de vue d'un système (qui est représenté par divers modèles) en une seule vue spécialisée (un modèle composé). Plusieurs approches ont été proposées pour aborder ce problème, mais elles présentent d'importantes limitations concernant l'efficacité, l'interopérabilité, et/ou les problèmes de synchronisation (principalement en raison du mécanisme de clonage d'éléments utilisé pour la création du modèle composé). Dans cet article nous proposons une nouvelle méthode de composition des modèles basée sur la virtualisation du mécanisme de composition. Dans notre approche, le modèle composé est en fait créé comme un modèle virtuel qui redirige toutes ses demandes d'accès et de manipulation directement à l'ensemble des modèles de base à partir desquelles il a été généré. Cela se fait d'une manière transparente pour le concepteur. Notre mécanisme améliore le processus de composition par rapport aux limitations mentionnées ci-dessus. La solution a été implémentée et validée dans un prototype développé sur EMF (Eclipse Modeling Framework)

    Virtual Composition of EMF Models

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    National audienceModel composition is a very important modeling task as it allows to combine various perspectives of a system (represented by various models) into a single specialized view (a composed model). Several approaches have been proposed to tackle this problem, but they present some important limitations concerning efficiency, interoperability, and/or synchronization issues (mainly due to the element cloning mechanism used to create the composed model). In this paper we propose a new model composition method based on the virtualization of the composition mechanism. In our approach, the composed model is in fact created as a virtual model that redirects all its model access and manipulation requests directly to the set of base models from which it was generated. This is done transparently for the designer. Our mechanism improves the composition process with relation to the limitations mentioned above. The solution has been implemented and validated in a prototype tool on top of EMF.La composition de modèles est une tâche de modélisation très importante car elle permet de combiner différents points de vue d'un système (qui est représenté par divers modèles) en une seule vue spécialisée (un modèle composé). Plusieurs approches ont été proposées pour aborder ce problème, mais elles présentent d'importantes limitations concernant l'efficacité, l'interopérabilité, et/ou les problèmes de synchronisation (principalement en raison du mécanisme de clonage d'éléments utilisé pour la création du modèle composé). Dans cet article nous proposons une nouvelle méthode de composition des modèles basée sur la virtualisation du mécanisme de composition. Dans notre approche, le modèle composé est en fait créé comme un modèle virtuel qui redirige toutes ses demandes d'accès et de manipulation directement à l'ensemble des modèles de base à partir desquelles il a été généré. Cela se fait d'une manière transparente pour le concepteur. Notre mécanisme améliore le processus de composition par rapport aux limitations mentionnées ci-dessus. La solution a été implémentée et validée dans un prototype développé sur EMF (Eclipse Modeling Framework)

    Conceptual Modeling in Human Resource Management: A Design Research Approach

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    In this paper, we introduce conceptual human resource modeling (CHRM) as a new methodical paradigm in HRM. To this end, we employ a design research approach. On a general level, we identify general tasks that CHRM can tackle and general solutions that it can provide. On a concrete level, we use the specific problem of employee assignment as an example and develop a specific CHRM solution for this HR task. By using the prototypical CHRM tool, we show how one can solve practical HR tasks based on CHRM. Finally, we discuss the lessons learned and implications for future research in CHRM

    Considerations about quality in model-driven engineering

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The 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 model-driven 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 modeling 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 16 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 25,000), 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 modeling 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 modeling 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.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. ISO/IEC/IEEE 42010:2011(E) (Revision of ISO/IEC 42010:2007 and IEEE Std 1471-2000) (pp. 1–46).Abran, A., Moore, J.W., Bourque, P., Dupuis, R., & Tripp, L.L. (2013). Guide to the Software Engineering Body of Knowledge (SWEBOK) version 3 public review. IEEE. ISO Technical Report ISO/IEC TR 19759.Agner, L.T.W., Soares, I.W., Stadzisz, P.C., & Simão, J.M. (2013). A brazilian survey on {UML} and model-driven practices for embedded software development. Journal of Systems and Software, 86(4), 997–1005. {SI} : Software Engineering in Brazil: Retrospective and Prospective Views.Amstel, M.F.V. (2010). The right tool for the right job: assessing model transformation quality. pages 69–74. Affiliation: Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, Netherlands. Cited By (since 1996):1.Aranda, J., Damian, D., & Borici, A. (2012). Transition to model-driven engineering: what is revolutionary, what remains the same?. In Proceedings of the 15th international conference on model driven engineering languages and systems, MODELS’12 (pp. 692–708). Berlin, Heidelberg: Springer.Arendt, T., & Taentzer, G. (2013). A tool environment for quality assurance based on the eclipse modeling framework. Automated Software Engineering, 20(2), 141–184.Atkinson, C., Bunse, C., & Wüst, J. (2003). Driving component-based software development through quality modelling, volume 2693. Cited By (since 1996):3.Baker, P., Loh, S., & Weil, F. (2005). Model-driven engineering in a large industrial context—motorola case study. In Briand, L., & Williams, C. (Eds.) Model Driven Engineering Languages and Systems, volume 3713 of Lecture Notes in Computer Science (pp. 476–491). Berlin, Heidelberg: Springer.Barišić, A., Amaral, V., Goulão, M., & Barroca, B. (2011). Quality in use of domain-specific languages: a case study. In Proceedings of the 3rd ACM SIGPLAN workshop on evaluation and usability of programming languages and tools, PLATEAU ’11 (pp. 65–72). New York: ACM.Becker, J., Bergener, P., Breuker, D., & Rackers, M. (2010). Evaluating the expressiveness of domain specific modeling languages using the bunge-wand-weber ontology. In 2010 43rd Hawaii international conference on system sciences (HICSS) (pp. 1–10).Bertrand Portier, L.A. (2009). Model driven development misperceptions and challenges.Bézivin, J., & Kurtev, I. (2005). Model-based technology integration with the technical space concept. In Proceedings of the Metainformatics Symposium: Springer.Brambilla, M. (2016). How mature is of model-driven engineering as an engineering discipline @ONLINE.Brambilla, M., & Fraternali, P. (2014). Large-scale model-driven engineering of web user interaction: The webml and webratio experience. Science of Computer Programming, 89 Part B(0), 71 – 87. Special issue on Success Stories in Model Driven Engineering.Brown, A. (2009). Simple and practical model driven architecture (mda) @ONLINE.Bruel, J.-M., Combemale, B., Ober, I., & Raynal, H. (2015). Mde in practice for computational science. Procedia Computer Science, 51, 660–669.Budgen, D., Burn, A.J., Brereton, O.P., Kitchenham, B.A., & Pretorius, R. (2011). Empirical evidence about the uml: a systematic literature review. Software: Practice and Experience, 41(4), 363–392.Burden, H., Heldal, R., & Whittle, J. (2014). Comparing and contrasting model-driven engineering at three large companies. In Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’14 (pp. 14:1–14:10). New York: ACM.Cabot, J. Has mda been abandoned (by the omg)?Cabot, J. (2009). Modeling will be commonplace in three years time @ONLINE.Cachero, C., Poels, G., Calero, C., & Marhuenda, Y. (2007). Towards a Quality-Aware Engineering Process for the Development of Web Applications. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/462, Ghent University, Faculty of Economics and Business Administration.Challenger, M., Kardas, G., & Tekinerdogan, B. (2015). A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems. Software Quality Journal, 1–41.Chaudron, M.V., Heijstek, W., & Nugroho, A. (2012). How effective is uml modeling? Software & Systems Modeling, 11(4), 571–580. J2: Softw Syst Model.Chenouard, R., Granvilliers, L., & Soto, R. (2008). Model-driven constraint programming. pages 236–246. Affiliation: CNRS, LINA, Universit de Nantes, France; Affiliation: Pontificia Universidad Catlica de, Valparaiso, Chile. Cited By (since 1996):8.Clark, T., & Muller, P.-A. (2012). Exploiting model driven technology: a tale of two startups. Software and Systems Modeling, 11(4), 481–493.Corneliussen, L. (2008). What do you think of model-driven software development?Costal, D., Gómez, C., & Guizzardi, G. (2011). Formal semantics and ontological analysis for understanding subsetting, specialization and redefinition of associations in uml. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6998 LNCS:189–203. cited By (since 1996)3.Cruz-Lemus, J.A., Maes, A., Género, M., Poels, G., & Piattini, M. (2010). The impact of structural complexity on the understandability of uml statechart diagrams. Information Sciences, 180(11), 2209–2220. Cited By (since 1996):14.Cuadrado, J.S., Izquierdo, J.L.C., & Molina, J.G. (2014). Applying model-driven engineering in small software enterprises. Science of Computer Programming, 89 Part B(0), 176 – 198. Special issue on Success Stories in Model Driven Engineering.Da Silva, A.R. (2015). Model-driven engineering: a survey supported by the unified conceptual model. Computer Languages Systems and Structures, 43, 139–155.Da Silva Teixeira, D.G.M., Quirino, G.K., Gailly, F., De Almeida Falbo, R., Guizzardi, G., & Perini Barcellos, M. (2016). PoN-S: a Systematic Approach for Applying the Physics of Notation (PoN), (pp. 432–447). Cham: Springer International Publishing.Davies, I., Green, P., Rosemann, M., Indulska, M., & Gallo, S. (2006). How do practitioners use conceptual modeling in practice? Data and Knowledge Engineering, 58(3), 358 – 380. Including the special issue : {ER} 2004ER 2004.Davies, J., Milward, D., Wang, C.-W., & Welch, J. (2015). Formal model-driven engineering of critical information systems. Science of Computer Programming, 103(0), 88 – 113. Selected papers from the First International Workshop on Formal Techniques for Safety-Critical Systems (FTSCS 2012).De Oca, I.M.-M., Snoeck, M., Reijers, H.A., & Rodríguez-Morffi, A. (2015). A systematic literature review of studies on business process modeling quality. Information and Software Technology, 58, 187–205.DenHaan, J. (2009). 8 reasons why model driven development is dangerous @ONLINE.DenHaan, J. (2010). Model driven engineering vs the commando pattern @ONLINE.DenHaan, J. (2011a). Why aren’t we all doing model driven development yet @ONLINE.DenHaan, J. (2011b). Why there is no future model driven development @ONLINE.Di Ruscio, D., Iovino, L., & Pierantonio, A. (2013). Managing the coupled evolution of metamodels and textual concrete syntax specifications. cited By (since 1996)0.Dijkman, R.M., Dumas, M., & Ouyang, C. (2008). Semantics and analysis of business process models in {BPMN}. Information and Software Technology, 50(12), 1281–1294.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ramos, I., & Fernández, L. (2011). A framework for the quality evaluation of mdwe methodologies and information technology infrastructures. International Journal of Human Capital and Information Technology Professionals, 2(4), 11–22.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., & Torres, A.H. (2010). A quality model in a quality evaluation framework for mdwe methodologies. pages 495–506. Affiliation: Departamento de Lenguajes y Sistemas Informíticos, University of Seville, Seville, Spain., Cited By (since 1996):1.Dubray, J.-J. (2011). Why did mde miss the boat?.Escalona, M.J., Gutiérrez, J.J., Pérez-Pérez, M., Molina, A., Domínguez-Mayo, E., & Domínguez-Mayo, F.J. (2011). Measuring the Quality of Model-Driven Projects with NDT-Quality, (pp. 307–317). New York: Springer.Espinilla, M., Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ross, M., & Staples, G. (2011). A Method Based on AHP to Define the Quality Model of QuEF (Vol. 123, pp. 685–694). Berlin, Heidelberg: Springer.Fabra, J., Castro, V.D., Álvarez, P., & Marcos, E. (2012). Automatic execution of business process models: exploiting the benefits of model-driven engineering approaches. Journal of Systems and Software, 85(3), 607–625. Novel approaches in the design and implementation of systems/software architecture.Falkenberg, E.D., Hesse, W., Lindgreen, P., Nilsson, B.E., Oei, J.L.H., Rolland, C., Stamper, R.K., Assche, F.J.M.V., Verrijn-Stuart, A.A., & Voss, K. (1996). Frisco: a framework of information system concepts. Technical report, The IFIP WG 8. 1 Task Group FRISCO.Fettke, P., Houy, C., Vella, A.-L., & Loos, P. (2012). Towards the Reconstruction and Evaluation of Conceptual Model Quality Discourses – Methodical Framework and Application in the Context of Model Understandability, volume 113 of Lecture Notes in Business Information Processing, chapter 28, pages 406–421, Springer, Berlin, Heidelberg.Finnie, S. (2015). Modeling community: Are we missing something?Fournier, C. (2008). Is uml [email protected], R., & Rumpe, B. (2007). Model-driven development of complex software: a research roadmap. In Future of Software Engineering, 2007, FOSE ’07 (pp. 37–54).Gallego, M., Giraldo, F.D., & Hitpass, B. (2015). Adapting the pbec-otss software selection approach for bpm suites: an application case. In 2015 34th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1–10).Galvão, I., & Goknil, A. (2007). Survey of traceability approaches in model-driven engineering. cited By (since 1996)22.Giraldo, F., España, S., Giraldo, W., & Pastor, O. (2015). Modelling language quality evaluation in model-driven information systems engineering: a roadmap. In 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS) (pp. 64–69).Giraldo, F., España, S., & Pastor, O. (2014). Analysing the concept of quality in model-driven engineering literature: a systematic review. In 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS) (pp. 1–12).Giraldo, F.D., España, S., & Pastor, O. (2016). Evidences of the mismatch between industry and academy on modelling language quality evaluation. arXiv: 1606.02025 .González, C., & Cabot, J. (2014). Formal verification of static software models in mde: a systematic review. Information and Software Technology, 56(8), 821–838. cited By (since 1996)0.González, C.A., Büttner, F., Clarisó, R., & Cabot, J. (2012). Emftocsp: a tool for the lightweight verification of emf models. pages 44–50. Affiliation: cole des Mines de Nantes, INRIA, LINA, Nantes, France; Affiliation: Universitat Oberta de Catalunya, Barcelona, Spain. Cited By (since 1996):1.Gorschek, T., Tempero, E., & Angelis, L. (2014). On the use of software design models in software development practice: an empirical investigation. Journal of Systems and Software, 95(0), 176– 193.Goulão, M., Amaral, V., & Mernik, M. (2016). Quality in model-driven engineering: a tertiary study. Software Quality Journal, 1–33.Grobshtein, Y., & Dori, D. (2011). Generating sysml views from an opm model: design and evaluation. Systems Engineering, 14(3), 327–340.Haan, J.d. (2008). 8 reasons why model-driven approaches (will) fail.Harel, D., & Rumpe, B. (2000). Modeling languages: Syntax, semantics and all that stuff, part i: The basic stuff, Israel. Technical report Jerusalem Israel.Harel, D., & Rumpe, B. (2004). Meaningful modeling: what’s the semantics of semantics? Computer, 37(10), 64–72.Hebig, R., & Bendraou, R. (2014). On the need to study the impact of model driven engineering on software processes. In Proceedings of the 2014 International Conference on Software and System Process, ICSSP 2014 (pp. 164–168). New York: ACM.Heidari, F., & Loucopoulos, P. (2014). Quality evaluation framework (qef): modeling and evaluating quality of business processes. International Journal of Accounting Information Systems, 15(3), 193–223. Business Process Modeling.Heymans, P., Schobbens, P.Y., Trigaux, J.C., Bontemps, Y., Matulevicius, R., & Classen, A. (2008). Evaluating formal properties of feature diagram languages. Software, IET, 2(3), 281–302. ID 2.Hindawi, M., Morel, L., Aubry, R., & Sourrouille, J.-L. (2009). Description and Implementation of a UML Style Guide (Vol. 5421, pp. 291–302). Berlin: Springer.Hoang, D. (2012). Current limitations of mdd and its implications @ONLINE.Hodges, W. (2013). Model theory Zalta, E.N. (Ed.) The Stanford Encyclopedia of Philosophy. Fall 2013 edition.Hutchinson, J., Rouncefield, M., & Whittle, J. (2011a). Model-driven engineering practices in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 633–642). New York: ACM.Hutchinson, J., Whittle, J., & Rouncefield, M. (2014). Model-driven engineering practices in industry: social, organizational and managerial factors that lead to success or failure. Science of Computer Programming, 89 Part B(0), 144–161. Special issue on Success Stories in Model Driven Engineering.Hutchinson, J., Whittle, J., Rouncefield, M., & Kristoffersen, S. (2011b). Empirical assessment of mde in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 471–480). New York: ACM.Igarza, I.M.H., Boada, D.H.G., & Valdés, A.P. (2012). Una introducción al desarrollo de software dirigido por modelos. Serie Científica, 5(3).ISO/IEC (2001). ISO/IEC 9126. Software engineering—Product quality. ISO/IEC.Izurieta, C., Rojas, G., & Griffith, I. (2015). Preemptive management of model driven technical debt for improving software quality. In Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures, QoSA’15 (pp. 31–36). New York: ACM.Jalali, S., & Wohlin, C. (2012). Systematic literature studies: Database searches vs. backward snowballing. In Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM’12 (pp. 29–38). New York: ACM.Kahraman, G., & Bilgen, S. (2013). A framework for qualitative assessment of domain-specific languages. Software & Systems Modeling, 1–22.Kessentini, M., Langer, P., & Wimmer, M. (2013). Searching models, modeling search: On the synergies of sbse and mde (pp. 51–54).Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.Kitchenham, B., Pfleeger, S., Pickard, L., Jones, P., Hoaglin, D., El Emam, K., & Rosenberg, J. (2002). Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, 28(8), 721–734.Klinke, M. (2008). Do you use mda/mdd/mdsd, any kind of model-driven approach? Will it be the future?Köhnlein, J. (2013). Eclipse diagram editors from a user’s perspective.Kolovos, D.S., Paige, R.F., & Polack, F.A. (2008). The grand challenge of scalability for model driven engineering. In Models in Software Engineering (pp. 48–53): Springer.Kolovos, D.S., Rose, L.M., Matragkas, N., Paige, R.F., Guerra, E., Cuadrado, J.S., De Lara, J., Ráth, I., Varró, D., Tisi, M., & Cabot, J. (2013). A research roadmap towards achieving scalability in model driven engineering. In Proceedings of the Workshop on Scalability in Model Driven Engineering, BigMDE’13 (pp. 2:1–2:10). New York: ACM.Krill, P. (2016). Uml to be ejected from microsoft visual studio (infoworld).Krogstie, J. (2012a). Model-based development and evolution of information systems: a quality approach, Springer Publishing Company, Incorporated.Krogstie, J. (2012b). Quality of modelling languages, (pp. 249–280). London: Springer.Krogstie, J. (2012c). Quality of models, (pp. 205–247). London: Springer.Krogstie, J. (2012d). Specialisations of SEQUAL, (pp. 281–326). London: Springer.Krogstie, J., Lindland, O.I., & Sindre, G. (1995). Defining quality aspects for conceptual models. In Proceedings of the IFIP International Working Conference on Information System Concepts: Towards a Consolidation of Views (pp. 216–231). London: Chapman & Hall, Ltd.Kruchten, P. (2000). The rational unified process: an introduction, 2nd edn. Boston: Addison-Wesley Longman Publishing Co., Inc.Kruchten, P., Nord, R., & Ozkaya, I. (2012). Technical debt: from metaphor to theory and practice. Software, IEEE, 29(6), 18–21.Kulkarni, V., Reddy, S., & Rajbhoj, A. (2010). Scaling up model driven engineering – experience and lessons learnt. In Petriu, D., Rouquette, N., & Haugen, y. (Eds.) Model Driven Engineering Languages and Systems, volume 6395 of Lecture Notes in Computer Science (pp. 331–345). Berlin, Heidelberg: Springer.Laguna, M.A., & Marqués, J.M. (2010). Uml support for designing software product lines: the package merge mechanism, 16(17), 2313–2332.Lange, C. (2007a). Model size matters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4364 LNCS:211–216. cited By (since 1996)1.Lange, C., & Chaudron, M. (2005). Managing Model Quality in UML-Based Software Development. In 13th IEEE International Workshop on Technology and Engineering Practice, 2005 (pp. 7–16).Lange, C., Chaudron, M.R.V., Muskens, J., Somers, L.J., & Dortmans, H.M. (2003). An empirical investigation in quantifying inconsistency and incompleteness of uml designs. In Incompleteness of UML Designs, Proceedings Workshop on Consistency Problems in UML-based Software Development, 6th International Conference on Unified Modeling Language, UML, 2003.Lange, C., DuBois, B., Chaudron, M., & Demeyer, S. (2006). An experimental investigation of uml modeling conventions. In Nierstrasz, O., Whittle, J., Harel, D., & Reggio, G. (Eds.) Model Driven Engineering Languages and Systems, volume 4199 of Lecture Notes in Computer Science (pp. 27–41). Berlin, Heidelberg: Springer.Lange, C.F.J., & Chaudron, M.R.V. (2006). Effe

    Supporting Automatic Interoperability in Model-Driven Development Processes

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    By analyzing the last years of software development evolution, it is possible to observe that the involved technologies are increasingly focused on the definition of models for the specification of the intended software products. This model-centric development schema is the main ingredient for the Model-Driven Development (MDD) paradigm. In general terms, the MDD approaches propose the automatic generation of software products by means of the transformation of the defined models into the final program code. This transformation process is also known as model compilation process. Thus, MDD is oriented to reduce (or even eliminate) the hand-made programming, which is an error-prone and time-consuming task. Hence, models become the main actors of the MDD processes: the models are the new programming code. In this context, the interoperability can be considered a natural trend for the future of model-driven technologies, where different modeling approaches, tools, and standards can be integrated and coordinated to reduce the implementation and learning time of MDD solutions as well as to improve the quality of the final software products. However, there is a lack of approaches that provide a suitable solution to support the interoperability in MDD processes. Moreover, the proposals that define an interoperability framework for MDD processes are still in a theoretical space and are not aligned with current standards, interoperability approaches, and technologies. Thus, the main objective of this doctoral thesis is to develop an approach to achieve the interoperability in MDD processes. This interoperability approach is based on current metamodeling standards, modeling language customization mechanisms, and model-to-model transformation technologies. To achieve this objective, novel approaches have been defined to improve the integration of modeling languages, to obtain a suitable interchange of modeling information, and to perform automatic interoperability verification.Giachetti Herrera, GA. (2011). Supporting Automatic Interoperability in Model-Driven Development Processes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11108Palanci

    A framework for evaluating the quality of modelling languages in MDE environments

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    This thesis presents the Multiple Modelling Quality Evaluation Framework method (hereinafter MMQEF), which is a conceptual, methodological, and technological framework for evaluating quality issues in modelling languages and modelling elements by the application of a taxonomic analysis. It derives some analytic procedures that support the detection of quality issues in model-driven projects, such as the suitability of modelling languages, traces between abstraction levels, specification for model transformations, and integration between modelling proposals. MMQEF also suggests metrics to perform analytic procedures based on the classification obtained for the modelling languages and artifacts under evaluation. MMQEF uses a taxonomy that is extracted from the Zachman framework for Information Systems (Zachman, 1987; Sowa and Zachman, 1992), which proposed a visual language to classify elements that are part of an Information System (IS). These elements can be from organizational to technical artifacts. The visual language contains a bi-dimensional matrix for classifying IS elements (generally expressed as models) and a set of seven rules to perform the classification. As an evaluation method, MMQEF defines activities in order to derive quality analytics based on the classification applied on modelling languages and elements. The Zachman framework was chosen because it was one of the first and most precise proposals for a reference architecture for IS, which is recognized by important standards such as the ISO 42010 (612, 2011). This thesis presents the conceptual foundation of the evaluation framework, which is based on the definition of quality for model-driven engineering (MDE). The methodological and technological support of MMQEF is also described. Finally, some validations for MMQEF are reported.Esta tesis presenta el método MMQEF (Multiple Modelling Quality Evaluation Framework), el cual es un marco de trabajo conceptual, metodológico y tecnológico para evaluar aspectos de calidad sobre lenguajes y elementos de modelado mediante la aplicación de análisis taxonómico. El método deriva procedimientos analíticos que soportan la detección de aspectos de calidad en proyectos model-driven tales como: idoneidad de lenguajes de modelado, trazabilidad entre niveles de abstracción, especificación de transformación de modelos, e integración de propuestas de modelado. MMQEF también sugiere métricas para ejecutar procedimientos analíticos basados en la clasificación obtenida para los lenguajes y artefactos de modelado bajo evaluación. MMQEF usa una taxonomía para Sistemas de Información basada en el framework Zachman (Zachman, 1987; Sowa and Zachman, 1992). Dicha taxonomía propone un lenguaje visual para clasificar elementos que hacen parte de un Sistema de Información. Los elementos pueden ser artefactos asociados a niveles desde organizacionales hasta técnicos. El lenguaje visual contiene una matriz bidimensional para clasificar elementos de Sistemas de Información, y un conjunto de siete reglas para ejecutar la clasificación. Como método de evaluación MMEQF define actividades para derivar analíticas de calidad basadas en la clasificación aplicada sobre lenguajes y elementos de modelado. El marco Zachman fue seleccionado debido a que éste fue una de las primeras y más precisas propuestas de arquitectura de referencia para Sistemas de Información, siendo ésto reconocido por destacados estándares como ISO 42010 (612, 2011). Esta tesis presenta los fundamentos conceptuales del método de evaluación basado en el análisis de la definición de calidad en la ingeniería dirigida por modelos (MDE). Posteriormente se describe el soporte metodológico y tecnológico de MMQEF, y finalmente se reportan validaciones.Aquesta tesi presenta el mètode MMQEF (Multiple Modelling Quality Evaluation Framework), el qual és un marc de treball conceptual, metodològic i tecnològic per avaluar aspectes de qualitat sobre llenguatges i elements de modelatge mitjançant l'aplicació d'anàlisi taxonòmic. El mètode deriva procediments analítics que suporten la detecció d'aspectes de qualitat en projectes model-driven com ara: idoneïtat de llenguatges de modelatge, traçabilitat entre nivells d'abstracció, especificació de transformació de models, i integració de propostes de modelatge. MMQEF també suggereix mètriques per executar procediments analítics basats en la classificació obtinguda pels llenguatges i artefactes de mode-lat avaluats. MMQEF fa servir una taxonomia per a Sistemes d'Informació basada en el framework Zachman (Zachman, 1987; Sowa and Zachman, 1992). Aquesta taxonomia proposa un llenguatge visual per classificar elements que fan part d'un Sistema d'Informació. Els elements poden ser artefactes associats a nivells des organitzacionals fins tècnics. El llenguatge visual conté una matriu bidimensional per classificar elements de Sistemes d'Informació, i un conjunt de set regles per executar la classificació. Com a mètode d'avaluació MMEQF defineix activitats per derivar analítiques de qualitat basades en la classificació aplicada sobre llenguatges i elements de modelatge. El marc Zachman va ser seleccionat a causa de que aquest va ser una de les primeres i més precises propostes d'arquitectura de referència per a Sistemes d'Informació, sent això reconegut per destacats estàndards com ISO 42010 (612, 2011). Aquesta tesi presenta els fonaments conceptuals del mètode d'avaluació basat en l'anàlisi de la definició de qualitat en l'enginyeria dirigida per models (MDE). Posteriorment es descriu el suport metodològic i tecnològic de MMQEF, i finalment es reporten validacions.Giraldo Velásquez, FD. (2017). A framework for evaluating the quality of modelling languages in MDE environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90628TESI

    On the Use of Alloy in Engineering Domain Specific Modeling Languages

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    Domain Specific Modeling Languages (DSMLs) tend to play a central role in modern design processes as they enable the effective involvement of domain experts by focusing on a particular problem domain while abstracting away technical details. In this thesis, we investigate the specification of DSMLs with a particular focus on domain expert driven validation. Mainly, we are interested in developing Alloy-based approaches, allowing the definition of specifications from which instances can be generated and given to the domain experts for the sake of validation. The work we present in this thesis can be divided into three parts: The first part concerns the definition and execution of model transformations defined in Alloy. While Alloy analysis can be used as an execution engine for model transformations, the analysis process is time consuming. Model transformations playing a central role in DSML definitions, the development of a new model transformation language, named F-Alloy, retaining the benefits of Alloy with the added property of being efficiently computable was necessary. The second part focuses on validation. In that domain, our first contribution is a novel approach to the validation of model transformations called Visualization-Based Validation (VBV). VBV relies on the review by domain experts of intuitive depictions of model transformation traces to validate model transformation specifications. The whole process is made efficient by the usage of hybrid analysis, a combination of Alloy analysis and F-Alloy interpretation, allowing to reduce the time needed to analyze model transformations to the time needed to analyze its source. Our second contribution in the validation area is the definition of an Alloy-based approach to the specification and validation of DSMLs and of a design process defining how DSMLs can be validated using Alloy analysis at each iteration of the process. More precisely, we present how the abstract syntax, concrete syntax and operational semantics of a DSML can be defined using Alloy and F-Alloy, and show that the validation of a DSML' s abstract syntax and semantics benefits from the application of its concrete syntax. The third and last part aims at bringing those contributions to the practical world. To achieve this we developed a tool named Lightning implementing the aforementioned contributions. This tool, which belongs to the category of language workbenches, has been successfully used in an inter-disciplinary collaboration to define the Robot Perception System Language (RPSL). Based on this definition of RPSL, a framework has been developed to allow the execution of so called design space explorations. This framework represents a successful application of our approach to the real world problem of having RPSL specifications validated by experts in robotics

    Mise en correspondance et gestion de la cohérence de modèles hétérogènes évolutifs

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    To understand and manipulate a complex system, it is necessary to apply the separation of concerns and produce separate parts. In Model Driven Engineering (MDE), these parts are represented by models qualified as partial models. In this context of multi-modeling, these models are called heterogeneous when they are described in separate modeling languages dedicated to different business domains: DSML (Domain Specific Modeling Language). Global model creation requires identifying existing correspondences between the elements of the partial models. However, in practice these correspondences are either incompletely identified or not sufficiently formalized to be maintained when the partial models evolve. This restricts their use and does not allow to fully exploit them for building the global model or for treating partial models evolution. The contribution of this thesis is twofold. The first contribution deals with a process for creating a global view of the system by means of a composition based on partial models matching. Identified correspondences between models elements are based on types of relationship instantiated from a metamodel of correspondences. This latter is extensible, depending on the considered application domain, and allows supporting the concepts related to this domain. Correspondences are firstly identified between meta-elements belonging to metamodels of the respective partial models. Correspondences between model elements are then obtained by a refinement mechanism, supported by an ad hoc Semantic Expression language: SED (Semantic Expression DSL). The composition is called “virtual” since elements represented in a correspondence are only references to elements belonging to partial models. Therefore, models interconnected by this correspondences form a virtual global model. The second contribution relates the consistency of the global model. Indeed, as models evolve over time, changing one or several elements involved in a correspondence, may cause the inconsistency of the global model. To maintain its consistency, we propose a second process enabling to automatically identify the changes, classify them and treat their impacts on the involved model elements. Management of repercussions is performed semi-automatically by the expert by means of strategies and weights. This work has been implemented through a support tool named HMCS (Heterogeneous Matching and Consistency management Suite) based on the Eclipse Platform. The approach has been validated and illustrated through a case study related to the management of a Hospital Emergency Service. This work was led in collaboration with the “CHU of Montpellier”.Pour permettre la compréhension et la manipulation d’un système complexe, le découpage en parties séparées est nécessaire. En Ingénierie Dirigée par les Modèles (ou Model Driven Engineering), ces parties sont représentées par des modèles, que nous qualifions de modèles partiels, dans la mesure où ils sont focalisés sur des domaines métiers distincts. Dans ce contexte de multi-modélisation, ces modèles sont dits hétérogènes quand ils sont décrits dans des langages de modélisation distincts dédiés à différents domaines métiers : DSML (Domain Specific Modeling language). La compréhension et l’exploitation efficace des connaissances relatives à un tel système supposent la construction d’un modèle global représentant son fonctionnement. La création du modèle global requiert l’identification des correspondances existant entre les éléments des différents modèles partiels. Dans la pratique, ces correspondances sont soit incomplètement identifiées, soit insuffisamment formalisées pour être maintenues lorsque les modèles partiels évoluent. Ceci limite leur utilisation et ne permet pas de les exploiter pleinement lors de la construction du modèle global ou du traitement de l’évolution des modèles partiels. L’apport de cette thèse est double. La première contribution est celle d’un processus permettant la création d’une vue globale du système par l’intermédiaire d’une composition fondée sur la mise en correspondance des modèles partiels. Les correspondances identifiées entres les éléments des modèles se basent sur des types de relations instanciées à partir d’un métamodèle de correspondance. Ce dernier est extensible (selon les spécificités du domaine d’application considéré) et permet de supporter les concepts relatifs à ce domaine. Les correspondances sont d’abord identifiées entre les méta-éléments des métamodèles respectifs des modèles partiels. Les correspondances entre les éléments de modèles sont ensuite obtenues par un mécanisme de raffinement, supporté par un langage d’expression sémantique ad hoc : SED (Semantic Expression DSL). La composition est dite « virtuelle » dans la mesure où les éléments figurant dans une correspondance ne sont que des références aux éléments appartenant aux modèles partiels. De ce fait, les modèles interconnectés par ces correspondances forment un modèle global virtuel. La seconde contribution est relative au maintien de la cohérence des modèles partiels et du modèle global. En effet, les modèles évoluant dans le temps, le changement d’un élément ou de plusieurs éléments participant à l’expression des correspondances, peut entrainer l’incohérence du modèle global. Pour maintenir la cohérence du modèle global, nous proposons un second processus permettant tout d’abord d’identifier automatiquement les changements réalisés ainsi que leurs classifications et leurs répercussions sur les éléments de modèles concernés. Par la suite, les différents cycles sont gérés à l’aide de l’expert puis une liste de changements est générée en fonction de la stratégie choisie et des coefficients de pondération. Enfin, le traitement des changements est réalisé de façon semi-automatique. Ce travail a été concrétisé par le développement d’un outil support nommé HMCS (Heterogeneous Matching and Consistency management Suite), basé sur la plateforme Eclipse. L’approche a été validée et illustrée à travers un cas d’étude portant sur la gestion du Service d'Urgence d'un hôpital. Ce travail a été mené en collaboration avec le CHU de Montpellier

    A Scholarship Approach to Model-Driven Engineering

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    Model-Driven Engineering is a paradigm for software engineering where software models are the primary artefacts throughout the software life-cycle. The aim is to define suitable representations and processes that enable precise and efficient specification, development and analysis of software. Our contributions to Model-Driven Engineering are structured according to Boyer\u27s four functions of academic activity - the scholarships of teaching, discovery, application and integration. The scholarships share a systematic approach towards seeking new insights and promoting progressive change. Even if the scholarships have their differences they are compatible so that theory, practice and teaching can strengthen each other.Scholarship of Teaching: While teaching Model-Driven Engineering to under-graduate students we introduced two changes to our course. The first change was to introduce a new modelling tool that enabled the execution of software models while the second change was to adapt pair lecturing to encourage the students to actively participate in developing models during lectures. Scholarship of Discovery: By using an existing technology for transforming models into source code we translated class diagrams and high-level action languages into natural language texts. The benefit of our approach is that the translations are applicable to a family of models while the texts are reusable across different low-level representations of the same model.Scholarship of Application: Raising the level of abstraction through models might seem a technical issue but our collaboration with industry details how the success of adopting Model-Driven Engineering depends on organisational and social factors as well as technical. Scholarship of Integration: Building on our insights from the scholarships above and a study at three large companies we show how Model-Driven Engineering empowers new user groups to become software developers but also how engineers can feel isolated due to poor tool support. Our contributions also detail how modelling enables a more agile development process as well as how the validation of models can be facilitated through text generation.The four scholarships allow for different possibilities for insights and explore Model-Driven Engineering from diverse perspectives. As a consequence, we investigate the social, organisational and technological factors of Model-Driven Engineering but also examine the possibilities and challenges of Model-Driven Engineering across disciplines and scholarships
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