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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

Abstract

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.

Keywords

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.

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Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

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

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