Advertisement

Non-human Modelers: Challenges and Roadmap for Reusable Self-explanation

  • Antonio Garcia-Dominguez
  • Nelly Bencomo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10748)

Abstract

Increasingly, software acts as a “non-human modeler” (NHM), managing a model according to high-level goals rather than a predefined script. To foster adoption, we argue that we should treat these NHMs as members of the development team. In our GrandMDE talk, we discussed the importance of three areas: effective communication (self-explanation and problem-oriented configuration), selection, and process integration. In this extended version of the talk, we will expand on the self-explanation area, describing its background in more depth and outlining a research roadmap based on a basic case study.

References

  1. 1.
    PROV Model Primer. W3C Working Group Note, World Wide Web Consortium, April 2013. https://www.w3.org/TR/2013/NOTE-prov-primer-20130430/#intuitive-overview-of-prov
  2. 2.
    Barmpis, K., Kolovos, D.: Hawk: towards a scalable model indexing architecture. In: Proceedings of the Workshop on Scalability in Model Driven Engineering, Budapest, Hungary. ACM (2013). http://dl.acm.org/citation.cfm?id=2487771
  3. 3.
    Buneman, P., Khanna, S., Tan, W.-C.: Why and where: a characterization of data provenance. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 316–330. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44503-X_20. http://dl.acm.org/citation.cfm?id=645504.656274 CrossRefGoogle Scholar
  4. 4.
    Burger, E., Mittelbach, V., Koziolek, A.: View-based and model-driven outage management for the smart grid. In: Proceedings of the 11th International Workshop on Models@run.time, Saint Malo, France, pp. 1–8. CEUR-WS.org, October 2016. http://ceur-ws.org/Vol-1742/MRT16_paper_1.pdf
  5. 5.
    Cleland-Huang, J., Gotel, O.C.Z., Huffman Hayes, J., Mäder, P., Zisman, A.: Software traceability: trends and future directions. In: Proceedings of the on Future of Software Engineering, FOSE 2014, pp. 55–69. ACM, New York (2014). http://doi.acm.org/10.1145/2593882.2593891
  6. 6.
    Debreceni, C., Ráth, I., Varró, D., De Carlos, X., Mendialdua, X., Trujillo, S.: Automated model merge by design space exploration. In: Stevens, P., Wąsowski, A. (eds.) FASE 2016. LNCS, vol. 9633, pp. 104–121. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49665-7_7 CrossRefGoogle Scholar
  7. 7.
    Garcia-Dominguez, A., Barmpis, K., Kolovos, D.S., Wei, R., Paige, R.F.: Stress-testing remote model querying APIs for relational and graph-based stores. Softw. Syst. Model. 1–29 (2017). https://link.springer.com/article/10.1007/s10270-017-0606-9
  8. 8.
    Garcia-Dominguez, A., Krikava, F., Rose, L.M. (eds.): Proceedings of the 9th Transformation Tool Contest, CEUR Workshop Proceedings, vol. 1758, December 2016. http://ceur-ws.org/Vol-1758/. ISSN 1613–0073
  9. 9.
    Garcia Paucar, L.H., Bencomo, N.: Runtime models based on dynamic decision networks: enhancing the decision-making in the domain of ambient assisted living applications. In: Proceedings of the 11th International Workshop on Models@run.time, Saint Malo, France, pp. 9–17. CEUR-WS.org, October 2016. http://eprints.aston.ac.uk/29790/
  10. 10.
    Giese, H., Bencomo, N., Pasquale, L., Ramirez, A.J., Inverardi, P., Wätzoldt, S., Clarke, S.: Living with uncertainty in the age of runtime models. In: Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U. (eds.) Models@run.time. LNCS, vol. 8378, pp. 47–100. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08915-7_3 CrossRefGoogle Scholar
  11. 11.
    Gotel, O.C.Z., Finkelstein, C.W.: An analysis of the requirements traceability problem. In: Proceedings of IEEE International Conference on Requirements Engineering, pp. 94–101, April 1994Google Scholar
  12. 12.
    Jouault, F., Allilaire, F., Bézivin, J., Kurtev, I., Valduriez, P.: ATL: a QVT-like transformation language. In: Companion to the 21st ACM SIGPLAN Symposium on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA 2006, pp. 719–720. ACM, New York (2006). http://doi.acm.org/10.1145/1176617.1176691
  13. 13.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003). http://ieeexplore.ieee.org/abstract/document/1160055/ MathSciNetCrossRefGoogle Scholar
  14. 14.
    Koegel, M., Helming, J.: EMFStore: a model repository for EMF models. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 2, pp. 307–308. ACM (2010)Google Scholar
  15. 15.
    Kolovos, D.S., Paige, R.F., Polack, F.A.C.: The epsilon transformation language. In: Vallecillo, A., Gray, J., Pierantonio, A. (eds.) ICMT 2008. LNCS, vol. 5063, pp. 46–60. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-69927-9_4 CrossRefGoogle Scholar
  16. 16.
    Matragkas, N.D., Kolovos, D.S., Paige, R.F., Zolotas, A.: A traceability-driven approach to model transformation testing. In: Proceedings of the Second Workshop on the Analysis of Model Transformations (AMT 2013), Miami, FL, USA, 29 September 2013 (2013). http://ceur-ws.org/Vol-1077/amt13_submission_7.pdf
  17. 17.
    Pagán, J.E., Cuadrado, J.S., Molina, J.G.: A repository for scalable model management. Softw. Syst. Model. 14(1), 219–239 (2015). https://link.springer.com/article/10.1007/s10270-013-0326-8 CrossRefGoogle Scholar
  18. 18.
    Paige, R.F., Drivalos, N., Kolovos, D.S., Fernandes, K.J., Power, C., Olsen, G.K., Zschaler, S.: Rigorous identification and encoding of trace-links in model-driven engineering. Softw. Syst. Model. 10(4), 469–487 (2011). http://link.springer.com/10.1007/s10270-010-0158-8 CrossRefGoogle Scholar
  19. 19.
    Wätzoldt, S., Giese, H.: Classifying distributed self-* systems based on runtime models and their coupling. In: Proceedings of the 9th International Workshop on Models at run.time, pp. 11–20 (2014). http://st.inf.tu-dresden.de/MRT14/papers/mrt14_submission_3.pdf

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Engineering and Applied ScienceAston UniversityBirminghamUK

Personalised recommendations