Advertisement

Requirements Engineering

, Volume 19, Issue 1, pp 63–80 | Cite as

Guidelines for using UML association classes and their effect on domain understanding in requirements engineering

  • Palash BeraEmail author
  • Joerg Evermann
Original Article

Abstract

The analysis and description of the application domain are important parts of the requirements engineering process. Domain descriptions are frequently represented as models in the de-facto standard unified modeling language (UML). Recent research has specified the semantics of various UML language elements for domain modeling, based on ontological considerations. In this paper, we empirically examine ontological modeling guidelines for the UML association construct, which plays a central role in UML class diagrams. Using an experimental study, we find that some, but not all, of the proposed guidelines lead to better application domain models. We use a process-tracing study to investigate in more detail the effects of ontological guidelines. The combined results indicate that ontological guidelines can improve the usefulness of UML class diagrams for describing the application domain, and thus have the potential to improve downstream system development activities and ultimately affect the successful information systems implementation.

Keywords

UML association class Conceptual model Domain understanding 

References

  1. 1.
    Wand Y, Weber R (1993) On the ontological expressiveness of information systems analysis and design grammars. J Inf Syst 3:217–237Google Scholar
  2. 2.
    Evermann J, Wand Y (2005) Toward formalizing domain modeling semantics and language syntax. IEEE Trans Softw Eng 31:21–37Google Scholar
  3. 3.
    Dobing B, Parsons J (2006) How UML is used? Commun ACM 49:109–113Google Scholar
  4. 4.
    Fettke P (2009) How conceptual modeling is used. Commun Assoc Inf Syst 25:571–592Google Scholar
  5. 5.
    Dobing B, Parsons J (2008) Dimensions of UML diagram use: a survey of practitioners. J Database Manag 19:1–18Google Scholar
  6. 6.
    Davies I, Green P, Rosemann M, Indulska M, Gallo S (2006) How do practitioners use conceptual modeling in practice? Data Knowl Eng 58:358–380Google Scholar
  7. 7.
    Parsons J (2011) An experimental study of the effects of representing property precedence on the comprehension of conceptual schemas. J AIS 12:441–462Google Scholar
  8. 8.
    Evermann J (2005) The association construct in conceptual modeling—an analysis using the Bunge ontological model. CAiSE, PortoGoogle Scholar
  9. 9.
    Milicev D (2007) On the semantics of associations and association ends in UML. IEEE Trans Softw Eng 33:238–251Google Scholar
  10. 10.
    Rumbaugh J, Blaha WP, Eddy F, Lorensen W (1991) Object oriented modeling and design. Prentice Hall, Englewood CliffsGoogle Scholar
  11. 11.
    Martin J, Odell J (1992) Object oriented analysis and design. Prentice Hall, Englewood CliffsGoogle Scholar
  12. 12.
    Bahrami A (1999) Object-oriented systems development using UML, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  13. 13.
    OM Group (2004) UML 2.0 superstructure specification, revised final adopted specification. Available: http://www.omg.org
  14. 14.
    Stevens P (2002) On the interpretation of binary associations in the unified modeling language. Softw Syst Model 1:68–79Google Scholar
  15. 15.
    Embley DW (1992) Object-oriented systems analysis: a model-driven approach. Prentice Hall, Englewood CliffsGoogle Scholar
  16. 16.
    Siegfried S (1995) Understanding object-oriented software engineering. IEEE Press, New YorkGoogle Scholar
  17. 17.
    Liu Z, He Z, Li J, Chen Y (2003) A relational model for formal object-oriented requirement analysis in UM. In: LNCS 2885. Springer, Berlin, pp 641–664Google Scholar
  18. 18.
    Evermann J, Wand Y (2005) Ontology based object-oriented domain modelling: fundamental concepts. Requir Eng J 10:146–160Google Scholar
  19. 19.
    Bunge M (1977) Ontology I: the furniture of the world, vol 3. D. Reidel, DodrechtzbMATHGoogle Scholar
  20. 20.
    Evermann J, Wand Y (2006) Ontological modelling rules for UML: an empirical assessment. J Comput Inf Syst 47:156–184Google Scholar
  21. 21.
    Poels G (2011) Understanding business domain models: the effect of recognizing resource-event–agent conceptual modeling structures. J Database Manag 22(4):69–101Google Scholar
  22. 22.
    Evermann J, Halimi H (2008) Associations and mutual properties—an experimental assessment. In: Americas conference on information systems, TorontoGoogle Scholar
  23. 23.
    Calder BJ, Phillips LW, Tybout AM (1981) Designing research for application. J Consum Res 8:197–207Google Scholar
  24. 24.
    Mayer R (2001) Multimedia learning. Cambridge University Press, CambridgeGoogle Scholar
  25. 25.
    Gemino A (1998) Comparing object oriented with structured analysis techniques in conceptual modeling. PhD thesis, Sauder School of Business, University of British Columbia, VancouverGoogle Scholar
  26. 26.
    Gemino A, Wand Y (2004) A framework for empirical evaluation of conceptual modeling techniques. Requir Eng J 9:248–260Google Scholar
  27. 27.
    Burton-Jones A, Meso P (2006) Conceptualizing systems for understanding: an empirical test of decomposition principles in object-oriented analysis. Inf Syst Res 17:38–60Google Scholar
  28. 28.
    Parsons J, Cole L (2005) What do the pictures mean? Guidelines for experimental evaluation of representation fidelity in diagrammatical conceptual modeling techniques. Data Knowl Eng 55(3):327–342Google Scholar
  29. 29.
    Allen MJ, Yen WM (2002) Introduction to measurement theory. Waveland Press, Long GroveGoogle Scholar
  30. 30.
    Nunnally J, Bernstein I (1994) Psychometric theory, 3rd edn. McGraw Hill, New YorkGoogle Scholar
  31. 31.
    Levine T, Krehbiel T, Berenson M (2010) Business statistics: a first course, 5th edn. Prentice-Hall, Englewood CliffsGoogle Scholar
  32. 32.
    Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, HillsdalezbMATHGoogle Scholar
  33. 33.
    Stephen O, Pearl B, David B (2006) Protocol analysis: a neglected practice. Commun ACM 49:117–122Google Scholar
  34. 34.
    Hungerford BC, Hevner A, Collins RW (2004) Reviewing software diagrams: a cognitive study. IEEE Trans Softw Eng 30:82–96Google Scholar
  35. 35.
    Evermann J (2008) An exploratory study of database integration processes. IEEE Trans Knowl Data Eng 20:99–115Google Scholar
  36. 36.
    Newell A, Simon HA (1972) Human problem solving. Prentice Hall, Englewood CliffsGoogle Scholar
  37. 37.
    Ericsson KA, Simon HA (1984) Protocol analysis: verbal reports as data. MIT Press, CambridgeGoogle Scholar
  38. 38.
    Gobet F, Charness N (2006) Chess and games. In: Ericsson KA, Charness N, Fletovich PJ, Hoffman RR (eds) The Cambridge handbook of expert performance. Cambridge University Press, New York, pp 41–67Google Scholar
  39. 39.
    Vessey I, Conger S (1994) Requirements specification: learning object, process, and data methodologies. Commun ACM 37:102–113Google Scholar
  40. 40.
    Bera P, Krasnoperova A, Wand Y (2010) Using OWL as a conceptual modeling language. J Database Manag 21:1–28Google Scholar
  41. 41.
    Vessey I, Galletta D (1991) Cognitive fit: an empirical study of information acquisition. Inf Syst Res 2:63–84Google Scholar
  42. 42.
    Gemino A, Wand Y (2005) Complexity and clarity in conceptual modeling: comparison of mandatory and optional properties. Data Knowl Eng 55:301–326Google Scholar
  43. 43.
    Shanks G, Tansley E, Nuredini J, Tobin D, Weber R (2008) Representing part-whole relations in conceptual modeling: an empirical evaluation. MIS Q 32:553–573Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Saint Louis UniversitySt. LouisUSA
  2. 2.Memorial University of NewfoundlandSt. John’sCanada

Personalised recommendations