Skip to main content
SpringerLink
Log in

A conceptual modeling quality framework

  • Published:
Software Quality Journal Aims and scope Submit manuscript

Abstract

The goal of any modeling activity is a complete and accurate understanding of the real-world domain, within the bounds of the problem at hand and keeping in mind the goals of the stakeholders involved. High-quality representations are critical to that understanding. This paper proposes a comprehensive Conceptual Modeling Quality Framework, bringing together two well-known quality frameworks: the framework of Lindland, Sindre, and Sølvberg (LSS) and that of Wand and Weber based on Bunge’s ontology (BWW). This framework builds upon the strengths of the LSS and BWW frameworks, bringing together and organizing the various quality cornerstones and then defining the many quality dimensions that connect one to another. It presents a unified view of conceptual modeling quality that can benefit both researchers and practitioners.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • Ambler, S. (2005). Elements of UML 2.0 style. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Armstrong, D. J., & Hardgrave, B. C. (2007). Understanding mindshift learning: The transition to object-oriented development. MIS Quarterly, 31(3), 453–474.

    Google Scholar 

  • Batini, C., Ceri, S., & Navathe, S. B. (1991). Conceptual database design: An entity-relationship approach. Redwood City: Benjamin/Cummings.

    Google Scholar 

  • Bolloju, N., & Leung, F. (2006). Assisting novice analysts in developing quality conceptual models with UML. Communications of the ACM, 49(7), 108–112.

    Article  Google Scholar 

  • Borgida, A., Greenspan, S., & Mylopoulos, J. (1985). Knowledge representation as the basis for requirements specifications. IEEE Computer, 15(4), 82–90.

    Article  Google Scholar 

  • Bunge, M. A. (1977). Ontology I: The furniture of the world (Vol. 3). Dordrecht: Reidel.

    MATH  Google Scholar 

  • Chen, P. P. (1976). The entity-relationship model—toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9–36.

    Article  Google Scholar 

  • Conradi, R., Mohagheghi, P., Arif, T., Hedge, L. C., Bundle, G. A., & Pedersen, A. (2003). Object-oriented reading techniques for inspection of UML models—an industrial experiment. Paper presented at the European conference on object-oriented programming (ECOOP’03), Darmstadt, Germany.

  • Deng, M., Stirewalt, R. E. K., & Cheng, B. H. C. (2005). Retrieval by construction: A traceability technique to support verification and validation of UML formalizations. International Journal of Software Engineering, 15(5), 837–872.

    Article  Google Scholar 

  • Evermann, J. (2005). Towards a cognitive foundation for knowledge representation. Information Systems Journal, 15, 147–178.

    Article  Google Scholar 

  • Evermann, J., & Wand, Y. (2006). Ontological modeling rules for UML: An empirical assessment. Journal of Computer Information Systems, 47, 14–29.

    Google Scholar 

  • Gemino, A., & Wand, Y. (2003). Evaluating modeling techniques based on models of learning. Communications of the ACM, 46(10), 79–84.

    Article  Google Scholar 

  • Gemino, A., & Wand, Y. (2005). Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties. Data & Knowledge Engineering, 55(3), 301–326.

    Article  Google Scholar 

  • Georgakopoulos, D., Hornick, M., & Sheth, A. (1995). An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases, 3, 119–153.

    Article  Google Scholar 

  • Gerard, G. J. (2005). The REA pattern, knowledge structures, and conceptual modeling performance. Journal of Information Systems, 19(2), 57–77.

    Article  Google Scholar 

  • Jacobson, I., Booch, G., & Rumbaugh, J. (1999). The unified software development process. Reading: Addison-Wesley.

    Google Scholar 

  • Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Khatri, V., Vessey, I., Ramesh, V., Clay, P., & Park, S.-J. (2006). Understanding conceptual schemas: Exploring the role of application and is domain knowledge. Information Systems Research, 17(1), 81–99.

    Article  Google Scholar 

  • Kiewkanya, M., & Muenchaisri, P. (2005). Measuring maintainability in early phase using aesthetic metrics. Paper presented at the 4th international conference on software engineering, parallel and distributed systems (SEPADS 2005), Salzburg, Austria.

  • Kong, J., Zhang, K., Dong, J., & Xu, D. (2009). Specifying behavioral semantics of UML diagrams through graph transformations. Journal of Systems and Software, 82(2), 292–306.

    Article  Google Scholar 

  • Krogstie, J. (2003). Evaluating UML using a generic quality framework. In L. Favre (Ed.), UML and the unified process. Hershey, PA: Idea Group Publishing.

    Google Scholar 

  • Krogstie, J., Sindre, G., & Jørgensen, H. (2006). Process models representing knowledge for action: A revised quality framework. European Journal of Information Systems, 15, 91–102.

    Article  Google Scholar 

  • Krogstie, J., & Sølvberg, A. (2003). Information systems engineering: Conceptual modeling in a quality perspective. Trondheim, Norway: Kompendiumforlaget.

    Google Scholar 

  • Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: The University of Chicago Press.

    Google Scholar 

  • Lange, C. F. J., Chaudron, M. R. V., & Muskens, J. (2006). UML software architecture and design description. IEEE Software, 23(2), 40–46.

    Article  Google Scholar 

  • Lindland, O. I., Sindre, G., & Sølvberg, A. (1994). Understanding quality in conceptual modeling. IEEE Software, 11(2), 42–49.

    Article  Google Scholar 

  • Lung, C. H., & Urban, J. E. (1995). An approach to the classification of domain models in support of analogical reuse. Paper presented at the SIGSOFT symposium on software reusability, Seattle.

  • Maiden, N. A. M., & Sutcliffe, A. G. (1992). Exploiting reusable specifications through analogy. Communications of the ACM, 35(55–64).

    Google Scholar 

  • Moody, D., & Shanks, G. (2003). Improving the quality of data models: Empirical validation of a quality management framework. Information Systems, 28, 619–650.

    Article  MATH  Google Scholar 

  • Moody, D., Shanks, G. G., & Darke, P. (1998). Improving the quality of entity relationship models—experience in research and practice. Paper presented at the 17th international conference on conceptual modeling, Singapore.

  • Moody, D., Sindre, G., Brasethvik, T., & Sølvberg, A. (2003). Evaluating the quality of information models: Empirical testing of a conceptual model quality framework. Paper presented at the International Conference on Software Engineering, Portland, OR.

  • Nelson, H. J., Monarchi, D. E., & Nelson, K. M. (2001). Ensuring the “goodness” of a conceptual representation. Paper presented at the European Conference on Software Measurement and Control (FESMA-DASMA), Heidelberg, Germany.

  • OMG. (2007a). OMG unified modeling language (OMG UML) infrastructure V2.1.2. Object Management Group.

  • OMG. (2007b). OMG unified modeling language (OMG UML) superstructure V2.1.2. Object Management Group.

  • Poels, G. (2009). Understanding business domain models: The effect of reorganizing resource-event-agent conceptual modeling structures. Unpublished FEB Working Paper. Ghent University.

  • Poels, G., Maes, A., Gailly, F., & Paemeleire, R. (2007). The pragmatic quality of resources-events-agents diagrams: an experimental evaluation. Information Systems Journal. doi:10.1111/j.1365-2575.2007.00253.x.

  • Purchase, H., Allder, J., & Carrington, D. (2002). Graph layout aesthetics in UML diagrams. Journal of Graph Algorithms and Applications, 6(3), 255–279.

    MATH  MathSciNet  Google Scholar 

  • Recker, J., Rosemann, M., & Krogstie, J. (2007). Ontology- versus pattern-based evaluation of process modeling languages: A comparison. Communications of the AIS, 20, 774–799.

    Google Scholar 

  • Russell, B. (1903). The principles of mathematics. New York: W. W. Norton and Company.

    MATH  Google Scholar 

  • Searle, J. R. (1995). The construction of social reality. New York: The Free Press.

    Google Scholar 

  • Shaft, T. M., & Vessey, I. (1995). The relevance of application domain knowledge - the case of computer-program comprehension. Information Systems Research, 6(3), 286–299.

    Article  Google Scholar 

  • Snoeck, M., & Poels, G. (2000). Improving the reuse possibilities of the behavioral aspects of object-oriented domain models. Lecture Notes in Computer Science (LNCS), 1920, 423–439.

    Article  Google Scholar 

  • St. Amant, R., McBride, S., & Ritter, F. (2006). AI support for building cognitive models. Paper presented at the twenty first national conference on artificial intelligence (AAAI-06), Menlo Park, CA.

  • Stamper, R. (1992). Signs, organisations, norms and information systems. Paper presented at the 3rd Australian conference on information systems, Wollongong.

  • Stamper, R., Liu, K., Hafkamp, M., & Ades, Y. (2000). Understanding the roles of signs and norms in organizations—a semiotic approach to information systems design. Behavior & Information Technology, 19(1), 15–27.

    Article  Google Scholar 

  • Unkelkar, B. (2005). Verification and validation for quality of UML 2.0 models. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • van Amstel, M. F., Lange, C. F. J., & Chaudron, M. R. V. (2007). Four automated approaches to analyze the quality of UML sequence diagrams. Paper presented at the 1st IEEE international workshop quality-oriented reuse of software (QUORS’07), Beijing.

  • Wagelaar, D., & Van Der Straeten, R. (2007). Platform ontology for the model driven architecture. European Journal of Information Systems, 16(4), 362–373.

    Article  Google Scholar 

  • Wand, Y., Storey, V. C., & Weber, R. (1999). An ontological analysis of the relationship construct in conceptual modeling. ACM Transactions on Database Systems, 24(4), 494–528.

    Article  Google Scholar 

  • Wand, Y., & Wang, R. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11), 86–95.

    Article  Google Scholar 

  • Wand, Y., & Weber, R. (1988). An ontological analysis of some fundamental information system concepts. Paper presented at the international conference on information systems (ICIS), Minneapolis, MN.

  • Wand, Y., & Weber, R. (1990a). An ontological model of an information system. IEEE Transactions on Software Engineering, 16(11), 1282–1292.

    Article  Google Scholar 

  • Wand, Y., & Weber, R. (1990b). Towards a theory of the deep structure of information systems. Paper presented at the international conference on information systems, Copenhagen, Denmark.

  • Wand, Y., & Weber, R. (1993). On the ontological expressiveness of information systems. IEEE Transactions on Software Engineering, 16(11), 217–237.

    Google Scholar 

  • Wand, Y., & Weber, R. (1995). On the deep structure of information-systems. Information Systems Journal, 5(3), 203–223.

    Article  Google Scholar 

  • Wand, Y., & Weber, R. (2002). Research commentary: Information systems and conceptual modeling—a research agenda. Information Systems Research, 13(4), 363–376.

    Article  Google Scholar 

Download references

Acknowledgments

This research has been funded by the following projects: MEDUSAS (CDTI-MICINN and FEDER IDI-20090557), ORIGIN (CDTI-MICINN and FEDER IDI-2010043(1-5)), PEGASO/MAGO (MICINN and FEDER, TIN2009-13718-C02-01), EECCOO (MICINN TRA2009_0074) and MECCA (JCMM PII2I09-0075-8394).

Author information

Authors and Affiliations

  1. Department of Management, Southern Illinois University, Carbondale, IL, 62901-4627, USA

    H. James Nelson

  2. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000, Ghent, Belgium

    Geert Poels

  3. Department of Technologies and Information Systems, University of Castilla-La Mancha, Paseo de la Universidad, 4, 13071, Ciudad Real, Spain

    Marcela Genero & Mario Piattini

Authors
  1. H. James Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geert Poels
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcela Genero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Piattini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. James Nelson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nelson, H.J., Poels, G., Genero, M. et al. A conceptual modeling quality framework. Software Qual J 20, 201–228 (2012). https://doi.org/10.1007/s11219-011-9136-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11219-011-9136-9

Keywords

Search

Navigation