Defining and validating metrics for assessing the understandability of entity-relationship diagrams

  • Authors:

  • Marcela Genero;Geert Poels;Mario Piattini

  • Affiliations:

  • ALARCOS Research Group, Department of Information Systems and Technologies, University of Castilla-La Mancha, Paseo de la Universidad, 4 - 13071 Ciudad Real, Spain;Management Informatics Research Unit, Faculty of Economics and Business Administration, Ghent University - UGent, Tweekerkenstraat 2, 9000 Ghent, Belgium;ALARCOS Research Group, Department of Information Systems and Technologies, University of Castilla-La Mancha, Paseo de la Universidad, 4 - 13071 Ciudad Real, Spain

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2008

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Abstract

Database and data model evolution cause significant problems in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams. In this paper we show that these easily calculated and objective metrics, measuring structural properties of ER diagrams, can be used as indicators of the understandability of the diagrams. Understandability is a key factor in determining maintainability as model modifications must be preceded by a thorough understanding of the model. The validation of the metrics as early understandability indicators opens up the way for an in-depth study of how structural properties determine conceptual data model understandability. It also allows building maintenance-related prediction models that can be used in conceptual data modelling practice.