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

  title={Defining and validating metrics for assessing the understandability of entity-relationship diagrams},
  author={Marcela Genero and Geert Poels and Mario Piattini},
  journal={Data Knowl. Eng.},
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… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 63 references

Foundations of measurement: geometrical

P. Suppes, D. Krantz, R. Luce, A. Tversky
Threshold and Probabilistic Representations, vol. 2, Academic Press, San Diego • 1989
View 3 Excerpts
Highly Influenced

Establishing software metric thresholds

V. French
International Workshop on Software Measurement (IWSM’99), 1999. 556 M. Genero et al. / Data & Knowledge Engineering 64 • 2008
View 1 Excerpt


M. Genero
Manso, A. Visaggio, G. Canfora, M. Piattini, Building a metric-based prediction model for UML class diagram maintainability, Empirical Software Engineering 12 (5) • 2007
View 1 Excerpt

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