Improving user productivity in modeling tools by explicitly modeling workflows

Regular Paper

Abstract

Software engineering aims to create software tools that allow people to solve particular problems in an easy and efficient way. In this regard, model-driven engineering (MDE) enables to generate software tools, by systematically modeling and transforming models. To do so, MDE relies on language workbenches: Integrated Development Environment for engineering modeling languages, designing models, executing them, and verifying them. However, the usability of these tools is far from efficient. Common MDE activities, such as creating a domain-specific language or developing a model transformation, are non-trivial and often require repetitive tasks. This results in unnecessary risings of development time. The goal of this paper is to increase the productivity of modelers in their daily activities by automating the tasks performed in current MDE tools. We propose an MDE-based solution where the user defines a reusable workflow that can be parameterized at run-time and executed. We have implemented workflows in the graphical modeling tool AToMPM. An empirical evaluation shows that the users’ productivity is significantly improved.

Keywords

Model-driven engineering Domain-specific language Enactment Model transformation User study 

References

  1. 1.
    Alajrami, S., Romanovsky, A., Watson, P., Roth, A.: Towards cloud-based software process modelling and enactment. In: Model-Driven Engineering on and for the Cloud, CloudMDE’14, vol. 1242, pp. 6–15 (2014)Google Scholar
  2. 2.
    Atkinson, C.: Meta-modelling for distributed object environments. In: Enterprise Distributed Object Computing Workshop, pp. 90–101. IEEE (1997)Google Scholar
  3. 3.
    Atkinson, C., Gerbig, R.: Melanie: multi-level modeling and ontology engineering environment. In: International Master Class on Model-Driven Engineering: Modeling Wizards, MW ’12, pp. 7:1–7:2. ACM (2012)Google Scholar
  4. 4.
    Atkinson, C., Kühne, T.: The essence of multilevel metamodeling. In: Gogolla, M., Kobryn, C. (eds.) Unified Modeling Language, Modeling Languages, Concepts, and Tools, LNCS, vol. 2185, pp. 19–33. Springer (2001)Google Scholar
  5. 5.
    Buzan, T.: The Ultimate Book of Mind Maps: Unlock Your Creativity, Boost Your Memory, Change Your Life. HarperCollins, New York (2006)Google Scholar
  6. 6.
    Cohen, J.: Statistical power analysis. Curr. Dir. Psychol. Sci. 1(3), 98–101 (1992)CrossRefGoogle Scholar
  7. 7.
    Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. Wiley, New York (1998)Google Scholar
  8. 8.
    de Lara, J., Guerra, E.: Deep meta-modelling with METADEPTH. In: Vitek, J. (ed.) Objects, Models, Components, Patterns, TOOLS’10, vol. 6141, pp. 1–20. Springer, New York (2010)CrossRefGoogle Scholar
  9. 9.
    Eclipse: modeling workflow engine 2. https://www.eclipse.org/Xtext/documentation/306_mwe2.html (2017). Accessed 6 Apr 2017
  10. 10.
    Ellis, P.D.: The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  11. 11.
    Erdweg, S., van der Storm, T., Völter, M., Boersma, M., Bosman, R., Cook, W.R., Gerritsen, A., Hulshout, A., Kelly, S., Loh, A., Konat, G.D.P., Molina, P.J., Palatnik, M., Pohjonen, R., Schindler, E., Schindler, K., Solmi, R., Vergu, V.A., Visser, E., van der Vlist, K., Wachsmuth, G.H., van der Woning, J.: The State of the Art in Language Workbenches. In: Erwig, M., Paige, R.F., Van Wyk, E. (eds.) Software Language Engineering, LNCS, vol. 8225, pp. 197–217. Springer, New York (2013)CrossRefGoogle Scholar
  12. 12.
    France, R., Rumpe, B.: Model-driven development of complex software: a research roadmap. In: 2007 Future of Software Engineering, pp. 37–54. IEEE Computer Society, Washington, DC (2007)Google Scholar
  13. 13.
    Gamboa, M.: Using workflows to automate activities in MDE tools. Master’s thesis, Université de Montréal (2016)Google Scholar
  14. 14.
    Gamboa, M.A., Syriani, E.: Automating activities in MDE tools. In: Model-Driven Engineering and Software Development, pp. 123–133. SciTePress (2016)Google Scholar
  15. 15.
    Gamboa, M.A., Syriani, E.: MODELSWARD 2016, revised and selected papers, CCIS, vol. 692, chap. Using Workflows to Automate Activities inMDE Tools, pp. 25–45. Springer, New York (2017)Google Scholar
  16. 16.
    Heidenreich, F., Johannes, J., Karol, S., Seifert, M., Wende, C.: Generative and transformational techniques in software engineering IV, LNCS. In: Lämmel, R., Saraiva, J., Visser, J. (eds.) Book Section Model-Based Language Engineering with EMFText, vol. 7680, pp. 322–345. Springer, New York (2013)Google Scholar
  17. 17.
    Jacob, F., Gray, J., Wynne, A., Liu, Y., Baker, N.: Domain-specific languages for composing signature discovery workflows. In: Workshop on Domain-Specific Modeling, pp. 61–64. ACM (2012)Google Scholar
  18. 18.
    Johnson, R., Woolf, B.: The Type Object Pattern. In: EuroPLoP (1996)Google Scholar
  19. 19.
    Kelly, S., Lyytinen, K., Rossi, M.: MetaEdit+A fully configurable multi-user and multi-tool CASE and CAME environment. In: Conference on Advanced Information Systems Engineering, LNCS, vol. 1080, pp. 1–21. Springer, New York (1996)Google Scholar
  20. 20.
    Kelly, S., Tolvanen, J.P.: Domain-Specific Modeling: Enabling Full Code Generation. Wiley, New York (2008)CrossRefGoogle Scholar
  21. 21.
    Kolovos, D.S., Paige, R.F., Polac, F.A., Rose, L.M.: Update transformations in the small with the Epsilon Wizard language. J. Object Technol. 6(9), 53–69 (2007)CrossRefGoogle Scholar
  22. 22.
    Kolovos, D.S., Paige, R.F., Polack, F.A.C.: Novel features in languages of the epsilon model management platform. In: Modeling in Software Engineering, pp. 69–73. ACM, New York (2008)Google Scholar
  23. 23.
    Lara, J.D., Guerra, E., Cuadrado, J.S.: When and how to use multilevel modelling. ACM Trans. Softw. Eng. Methodol. 24(12), 1–46 (2014)CrossRefGoogle Scholar
  24. 24.
    Leblebici, E., Anjorin, A., Schürr, A.: Developing eMoflon with eMoflon. In: Di Ruscio, D., Varró, D. (eds.) Theory and Practice of Model Transformations, LNCS, vol. 8568, pp. 138–145. Springer, New York (2014)Google Scholar
  25. 25.
    Lecerof, A., Paternò, F.: Automatic support for usability evaluation. IEEE Trans. Softw. Eng. 24(10), 863–888 (1998)CrossRefGoogle Scholar
  26. 26.
    Ledeczi, A., Maroti, M., Bakay, A., Karsai, G., Garrett, J., Thomason, C., Nordstrom, G., Sprinkle, J., Volgyesi, P.: The generic modeling environment. In: Workshop on Intelligent Signal Processing, Budapest, Hungary, WISP ’01, vol. 17, p. 1 (2001)Google Scholar
  27. 27.
    Lúcio, L., Amrani, M., Dingel, J., Lambers, L., Salay, R., Selim, G.M., Syriani, E., Wimmer, M.: Model transformation intents and their properties, pp. 1–38. Software and Systems Modeling, New York (2014)Google Scholar
  28. 28.
    Lucio, L., Mustafiz, S., Denil, J., Vangheluwe, H., Jukss, M.: FTG+PM: an integrated framework for investigating model transformation chains. In: SDL 2013: Model-Driven Dependability Engineering, LNCS, vol. 7916, pp. 182–202. Springer, New York (2013)Google Scholar
  29. 29.
    Mahmud, M., Abdullah, S., Hosain, S.: GWDL: a graphical workflow definition language for business workflows. In: Gaol, F. (ed.) Recent Progress in Data Engineering and Internet Technology, LNCS, vol. 156, pp. 205–210. Springer, New York (2013)CrossRefGoogle Scholar
  30. 30.
    Martin, D., Wutke, D., Leymann, F.: A novel approach to decentralized workflow enactment. In: Enterprise Distributed Object Computing, pp. 127–136. IEEE (2008)Google Scholar
  31. 31.
    Mernik, M., Heering, J., Sloane, A.M.: When and how to develop domain-specific languages. ACM Comput. Surv. 37(4), 316–344 (2005)CrossRefGoogle Scholar
  32. 32.
    Alves, A., Arkin, A., Askary, S., Bloch, B., Curbera, F., Ford, M., Goland, Y., Gulzar, A. Kartha, N., Liu, C.K., et al.: OASIS: Web Services Business Process Execution Language, 2nd edn. (2007)Google Scholar
  33. 33.
    Object Management Group.: OMG: Software and Systems Process Engineering Metamodel specification, 2.0 edn. (2008)Google Scholar
  34. 34.
    Object Management Group.: OMG: Information technology—Object Management Group Unified Modeling Language, Superstructure ISO/IEC 19505-2 (2012)Google Scholar
  35. 35.
    Rivera, J.E., Ruiz-González, D., López-Romero, F., Bautista, J.M.: Wires*: a tool for orchestrating model transformations. Jornadas de Ingeniería del Software y Bases de Datos pp. 158–161 (2009)Google Scholar
  36. 36.
    Russell, N., van der Aalst, W., ter Hofstede, A.: Workflow exception patterns. In: Dubois, E., Pohl, K. (eds.) Advanced Information Systems Engineering, LNCS, vol. 4001, pp. 288–302. Springer, New York (2006)CrossRefGoogle Scholar
  37. 37.
    Russell, N., van der Aalst, W., ter Hofstede, A., Edmond, D.: Workflow resource patterns: identification, representation and tool support. In: Pastor, O., Falcão e Cunha, J. (eds.) Advanced Information Systems Engineering, LNCS, vol. 3520, pp. 216–232. Springer, New York (2005)Google Scholar
  38. 38.
    Russell, N., van der Aalst, W., ter Hofstede, A., Mulyar, N.: Workflow control-flow patterns: a revised view. Technical report BPM-06-22, BPM Center (2006)Google Scholar
  39. 39.
    Schmidt, D.C.: Model-driven engineering. IEEE Comput. 39(2), 25–31 (2006)CrossRefGoogle Scholar
  40. 40.
    Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples). Biometrika 52(3/4), 591–611 (1965)MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF: Eclipse Modeling Framework, 2nd edn. Addison Wesley Professional, Boston (2008)Google Scholar
  42. 42.
    Syriani, E., Ergin, H.: Operational semantics of UML activity diagram: an application in project management. In: RE 2012 Workshops, pp. 1–8. IEEE (2012)Google Scholar
  43. 43.
    Syriani, E., Kienzle, J., Vangheluwe, H.: Exceptional transformations. In: Tratt, L., Gogolla, M. (eds.) Theory and Practice of Model Transformation, LNCS, vol. 6142, pp. 199–214. Springer, New York (2010)CrossRefGoogle Scholar
  44. 44.
    Syriani, E., Vangheluwe, H.: A modular timed model transformation language. J. Softw. Syst. Model. 12(2), 387–414 (2011)CrossRefGoogle Scholar
  45. 45.
    Syriani, E., Vangheluwe, H., LaShomb, B.: T-Core: a framework for custom-built transformation languages. Softw. Syst. Model. 14(3), 1215–1243 (2015)CrossRefGoogle Scholar
  46. 46.
    Syriani, E., Vangheluwe, H., Mannadiar, R., Hansen, C., Van Mierlo, S., Ergin, H.: AToMPM: A web-based modeling environment. In: Invited Talks, Demonstration Session, Poster Session, and ACM Student Research Competition, MODELS’13, vol. 1115, pp. 21–25. CEUR-WS.org (2013)Google Scholar
  47. 47.
    Whittle, J., Hutchinson, J., Rouncefield, M.: The state of practice in model-driven engineering. IEEE Softw. 31(3), 79–85 (2014)CrossRefGoogle Scholar
  48. 48.
    WMC: terminology and glossary. Technical report WFMC-TC-1011, Workflow Management Coalition (1999)Google Scholar
  49. 49.
    WMC: process definition interface—XML process definition language 2.00. Technical report WFMC-TC-1025, Workflow Management Coalition (2005)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Université de MontréalMontrealCanada

Personalised recommendations