Requirements Engineering

, Volume 9, Issue 4, pp 261–270 | Cite as

The effect of the number of concepts on the readability of schemas: an empirical study with data models

  • Akhilesh Bajaj
Original Article


The number of concepts in a model has been frequently used in the literature to measure the ease of use in creating model schemas. However, to the best of our knowledge, nobody has looked at its effect on the readability of the model schemas after they have been created. The readability of a model schema is important in situations where the schemas are created by one team of analysts and read by other analysts, system developers, or maintenance administrators. Given the recent trend of models with increasing numbers of concepts such as the unified modeling language (UML), the effect of the number of concepts (NOC) on the readability of schemas has become increasingly important. In this work, we operationalize readability along three dimensions: effectiveness, efficiency, and learnability. We draw on the Bunge Wand Weber (BWW) framework, as well as the signal detection recognition theory and the ACT theory from cognitive psychology to formulate hypotheses and conduct an experiment to study the effects of the NOC in a data model on these readability dimensions. Our work makes the following contributions: (a) it extends the operationalization of the readability construct, and (b) unlike earlier empirical work that has focused exclusively on comparing models that differ along several dimensions, this work proposes an empirical methodology that isolates the effect of a model-independent variable (the NOC) on readability. From a practical perspective, our findings have implications both for creators of new models, as well as for practitioners who use currently available models for creating schemas to communicate requirements during the entire lifecycle of a system.


Treatment Level Unify Modeling Language Model Schema Semantic Network Successive Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Amberg MA (1996) A pattern oriented approach to a methodical evaluation of modeling methods. Aust J Inf Syst 4(1):3–10Google Scholar
  2. 2.
    Anderson JR (1978) Arguments concerning representations for mental imagery. Psychol Rev 85:249–277CrossRefGoogle Scholar
  3. 3.
    Anderson JR (1995) Cognitive psychology and its implications, 5th edn. W.H. Freeman, New YorkGoogle Scholar
  4. 4.
    Bajaj A, Ram S (1996) A content specification for business process models. Aust J Inf Syst 4(1):22–31CrossRefGoogle Scholar
  5. 5.
    Batra D, Srinivasan A (1992) A review and analysis of the usability of data modeling environments. Int J Man Mach Stud 36:395–417CrossRefGoogle Scholar
  6. 6.
    Bock D, Ryan T (1993) Accuracy in modeling with extended entity relationship and O–O data models. J Database Manag 4(4):30–39Google Scholar
  7. 7.
    Booch G (1994) Object oriented analysis and design with applications. Benjamin/Cummings, Redwood CityGoogle Scholar
  8. 8.
    Booch G, Ivar J, James R (1997) UML Distilled. Addison Wesley, ReadingGoogle Scholar
  9. 9.
    Brosey M, Schneiderman B (1978) Two experimental comparisons of relational and hierarchical database models. Int J Man Mach Stud 10:625–637Google Scholar
  10. 10.
    Castellini X (1998) Evaluation of models defined with charts of concepts: application to the UML model. In: Paper read at third workshop on the evaluation of modeling methods in systems analysis and design, in concjunction with CAiSE, at PisaGoogle Scholar
  11. 11.
    Chen PP (1976) The entity-relationship model: towards a unified model of data. ACM Trans Database Syst 1(1):9–36CrossRefGoogle Scholar
  12. 12.
    Daniel K, Hirshleifer D, Subrahmanyan A (1998) Investor psychology and security marlet under- and over-confidence. J Finance 53:1839–1885CrossRefGoogle Scholar
  13. 13.
    Egan JP (1958) Recognition memory and the operating characteristics. Indiana University, BloomingtonGoogle Scholar
  14. 14.
    Fromkin HL, Streufert S (1976) Laboratory experimentation. In: Dunnette MD (ed) Handbook of industrial psychology. Rand-Mcnally, ChicagoGoogle Scholar
  15. 15.
    Gemino A, Wand Y (2001) Towards common dimensions in empirical comparisons of conceptual modeling techniques. In: Paper read at 7th CAiSE/IFIP-WG8.1 international workshop on the evaluation of modeling methods in systems analysis and design, TorontoGoogle Scholar
  16. 16.
    Hardgrave BC, Dalal N (1995) Comparing object oriented and extended entity relationship models. J Database Manag 6(3):15–21Google Scholar
  17. 17.
    Juhn S, Naumann JD (1985) The effectiveness of data representation characteristics on user validation. In: Paper read at international conference on information systems, IndianopolisGoogle Scholar
  18. 18.
    Kahnemann D, Slovic P, Tversky A (1982) Judgment under uncertainty: heuristics and biases. Cambridge University Press, LondonGoogle Scholar
  19. 19.
    Kim Y-G, March SE (1995) Comparing data modeling formalisms. Commun ACM 38(6):103–113CrossRefGoogle Scholar
  20. 20.
    Kramer B, Luqi (1991) Towards former models of software engineering processes. J Syst Softw 15:63–74CrossRefGoogle Scholar
  21. 21.
    Long De BJ, Shleifer A, Summers LH, Waldmann R (1990) Noise trader risk in financial markets. J Polit Econ 98:703–738CrossRefGoogle Scholar
  22. 22.
    Mantha RW (1987) Data flow and data structure modeling for database requirements determination: a comparative study. MIS Q December:531–545Google Scholar
  23. 23.
    Marcos E, Cervera J, Fernandez L (1999) Evaluation of data models: a complexity metric. In: Paper read at 4th Caise/IFIP 8.1 international workshop on evaluation of modeling methods in systems analysis and design, HeidelbergGoogle Scholar
  24. 24.
    Moynihan A (1996) An attempt to compare OO and functional decomposition in communicating information system functionality to users. In: Paper read at workshop on evaluation of modeling methods in systems analysis and design, CAiSE, Heraklion, Crete, 20–24 May 1996Google Scholar
  25. 25.
    Nielsen J (1993) Usability engineering. Academic, New YorkGoogle Scholar
  26. 26.
    Olle TW (1986) In: Proceedings of the IFIP WG 8.1 working conference on the comparative review of ISD methodologies: improving the practice, Borth HollandPubMedGoogle Scholar
  27. 27.
    Palvia PC, Liao C, To PL (1992) The impact of conceptual models on end-user performance. J Database Manag 3(4):4–15Google Scholar
  28. 28.
    Pearson PD, Johnson DD (1978) Teaching reading comprehension. Holt, New YorkGoogle Scholar
  29. 29.
    Peleg M, Dori D (2000) The model multiplicity problem: experimenting with real time specification methods. IEEE Trans Softw Eng 26(6):1–18CrossRefGoogle Scholar
  30. 30.
    Reed SK (1988) Cognition theory and applications. Brooks/Cole Publishing, BelmontGoogle Scholar
  31. 31.
    Reeves WW (1996) Cognition and complexity: the cognitive science of managing complexity. Scarecrow Press, LanhamGoogle Scholar
  32. 32.
    Rossi M, Brinkkemper S (1996) Complexity metrics for systems development methods and techniques. Inf Syst 21(2):209–227CrossRefGoogle Scholar
  33. 33.
    Shneiderman B (1998) Designing the user interface, 3rd edn. Addison Wesley, ReadingGoogle Scholar
  34. 34.
    Shoval P, Even-Chaime M (1987) Database schema design: an experimental comparison between normalization and information analysis. Database 18(3):30–39Google Scholar
  35. 35.
    Shoval P, Frummerman I (1994) OO and EER schemas: a comparison of user comprehension. J Database Manag 5(4):28–38Google Scholar
  36. 36.
    Siau K, Cao Q (2001) Unified modeling language (UML)—A complexity analysis. J Database Manag Jan–Mar:26–34Google Scholar
  37. 37.
    Svensson O (1981) Are we all less risky and more skilful than our fellow drivers? Acta Psychologica 47:143–148CrossRefGoogle Scholar
  38. 38.
    Topi H, Ramesh V (2002) Human factors on research on data modeling: a review of prior research, an extended framework and future research directions. J Database Manag 13(2):3–19Google Scholar
  39. 39.
    Wand Y, Weber R (1995) On the deep structure of information systems. Inf Syst J 5:203–223Google Scholar
  40. 40.
    Wand Y, Weber R (2002) Information systems and conceptual modeling: a research agenda. Inf Syst Res 13(4):363–376Google Scholar
  41. 41.
    Weber R (1997) Ontological foundations of information systems. Coopers and Lybrand, MelbourneGoogle Scholar
  42. 42.
    Wonnacott TH, Wonnacott RJ (1984) Introductory statistics for business and economics, 3rd edn. Wiley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.The University of TulsaTulsa U.S.A.

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