Skip to main content
SpringerLink
Log in

Experimental Practices for Measuring the Intuitive Comprehensibility of Modeling Constructs: An Example Design

  • Conference paper
  • First Online:
Advances in Conceptual Modeling (ER 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12584))

Included in the following conference series:

Abstract

Conceptual model comprehensibility has attracted the interest of many experimental researchers over the past decades. Several studies have employed a variety of definitions and operationalizations of the comprehensibility construct as well as procedures for measuring it on a variety of model types. Intuitive comprehensibility is a specialization of the construct, referring to model or language comprehensibility exhibited by partially trained users. We present an experimental design for measuring the intuitive comprehensibility of a proposed extension to a goal modeling language as a means for reviewing experimental practices we have followed for similar studies in the past. Through such review, we hope to demonstrate the possibility of experimental design and technique reusability and its role as a motivating factor for more experimentation within the conceptual modeling research community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alothman, N., Zhian, M., Liaskos, S.: User perception of numeric contribution semantics for goal models: an exploratory experiment. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 451–465. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69904-2_34

    Chapter  Google Scholar 

  2. Amyot, D., Mussbacher, G.: User requirements notation: the first ten years, the next ten years (invited paper). J. Softw. 6(5), 747–768 (2011)

    Article  Google Scholar 

  3. Bork, D., Schrüffer, C., Karagiannis, D.: Intuitive understanding of domain-specific modeling languages: proposition and application of an evaluation technique. In: Laender, A.H.F., Pernici, B., Lim, E.-P., de Oliveira, J.P.M. (eds.) ER 2019. LNCS, vol. 11788, pp. 311–319. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33223-5_26

    Chapter  Google Scholar 

  4. Crump, M.J.C., McDonnell, J.V., Gureckis, T.M.: Evaluating Amazon’s mech. Turk as a tool for experimental behavioral research. PLoS One 8(3), 1–18 (2013)

    Article  Google Scholar 

  5. Dalpiaz, F., Franch, X., Horkoff, J.: iStar 2.0 language guide. The Computing Research Repository (CoRR) abs/1605.0 (2016)

    Google Scholar 

  6. Evans, J.S.B.T.: Dual-processing accounts of reasoning, judgment, and social cognition. Annu. Rev. Psychol. 59(1), 255–278 (2008)

    Article  Google Scholar 

  7. Falessi, D., et al.: Empirical software engineering experts on the use of students and professionals in experiments. Empir. Softw. Eng. 23(1), 452–489 (2017). https://doi.org/10.1007/s10664-017-9523-3

    Article  Google Scholar 

  8. Gonçalves, E., Almendra, C., Goulão, M., Araújo, J., Castro, J.: Using empirical studies to mitigate symbol overload in iStar extensions. Softw. Syst. Model. 19(3), 763–784 (2019). https://doi.org/10.1007/s10270-019-00770-9

    Article  Google Scholar 

  9. Guizzardi, G.: Ontological foundations for structural conceptual models. Ph.D. thesis, University of Twente (2005)

    Google Scholar 

  10. Hadar, I.: When intuition and logic clash: the case of the object-oriented paradigm. Sci. Comput. Program. 78(9), 1407–1426 (2013)

    Article  Google Scholar 

  11. Houy, C., Fettke, P., Loos, P.: Understanding understandability of conceptual models – what are we actually talking about? In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 64–77. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34002-4_5

    Chapter  Google Scholar 

  12. Jošt, G., Huber, J., Heričko, M., Polančič, G.: An empirical investigation of intuitive understandability of process diagrams. Comput. Stand. Interfaces 48, 90–111 (2016)

    Article  Google Scholar 

  13. Krogstie, J., Sindre, G., Jørgensen, H.: Process models representing knowledge for action: a revised quality framework. Eur. J. Inf. Syst. 15(1), 91–102 (2006)

    Article  Google Scholar 

  14. Liaskos, S., Dundjerovic, T., Gabriel, G.: Comparing alternative goal model visualizations for decision making: an exploratory experiment. In: Proceedings of the 33rd ACM Symposium on Applied Computing (SAC 2018), Pau, France, pp. 1272–1281 (2018)

    Google Scholar 

  15. Liaskos, S., Jaouhar, I.: Towards a framework for empirical measurement of conceptualization qualities. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 512–522. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_38

    Chapter  Google Scholar 

  16. Liaskos, S., Khan, S.M., Soutchanski, M., Mylopoulos, J.: Modeling and reasoning with decision-theoretic goals. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) ER 2013. LNCS, vol. 8217, pp. 19–32. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41924-9_3

    Chapter  Google Scholar 

  17. Liaskos, S., Ronse, A., Zhian, M.: Assessing the intuitiveness of qualitative contribution relationships in goal models: an exploratory experiment. In: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2017), Toronto, Ontario, pp. 466–471 (2017)

    Google Scholar 

  18. Liaskos, S., Tambosi, W.: Factors affecting comprehension of contribution links in goal models: an experiment. In: Laender, A.H.F., Pernici, B., Lim, E.-P., de Oliveira, J.P.M. (eds.) ER 2019. LNCS, vol. 11788, pp. 525–539. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33223-5_43

    Chapter  Google Scholar 

  19. Mair, P., Wilcox, R.: Robust statistical methods in R using the WRS2 package. Behav. Res. Methods 52(2), 464–488 (2019). https://doi.org/10.3758/s13428-019-01246-w

    Article  Google Scholar 

  20. Maxwell, S.E., Delaney, H.D.: Designing Experiments and Analyzing Data, 2nd edn. Taylor and Francis Group, LLC, New York (2004)

    MATH  Google Scholar 

  21. Moody, D.L.: The “Physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)

    Article  Google Scholar 

  22. Roelens, B., Bork, D.: An evaluation of the intuitiveness of the PGA modeling language notation. In: Nurcan, S., Reinhartz-Berger, I., Soffer, P., Zdravkovic, J. (eds.) BPMDS/EMMSAD -2020. LNBIP, vol. 387, pp. 395–410. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49418-6_27

    Chapter  Google Scholar 

  23. Rosnow, R.L., Rosenthal, R.: Beginning Behavioral Research: A Conceptual Primer, 6th edn. Pearson Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  24. Wand, Y., Weber, R.: On the ontological expressiveness of information systems analysis and design grammars. Inf. Syst. J. 3(4), 217–237 (1993)

    Article  Google Scholar 

  25. Yu, E.S.K.: Towards modelling and reasoning support for early-phase requirements engineering. In: Proceedings of the 3rd IEEE International Symposium on Requirements Engineering (RE 1997), Annapolis, MD, pp. 226–235 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology, York University, 4700 Keele St., Toronto, M3J 1P3, Canada

    Sotirios Liaskos, Mehrnaz Zhian & Ibrahim Jaouhar

Authors
  1. Sotirios Liaskos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehrnaz Zhian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibrahim Jaouhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sotirios Liaskos .

Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, SA, Australia

    Georg Grossmann

  2. University of Arizona, Tucson, AZ, USA

    Sudha Ram

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liaskos, S., Zhian, M., Jaouhar, I. (2020). Experimental Practices for Measuring the Intuitive Comprehensibility of Modeling Constructs: An Example Design. In: Grossmann, G., Ram, S. (eds) Advances in Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12584. Springer, Cham. https://doi.org/10.1007/978-3-030-65847-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65847-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65846-5

  • Online ISBN: 978-3-030-65847-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation