Empirical Validation of Measures for UML Class Diagrams: A Meta-Analysis Study

  • Authors:

  • M. Esperanza Manso;José A. Cruz-Lemus;Marcela Genero;Mario Piattini

  • Affiliations:

  • GIRO Research Group, Department of Computer Science, University of Valladolid, Valladolid, Spain 47011;ALARCOS Research Group, Department of Technologies and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain 13071;ALARCOS Research Group, Department of Technologies and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain 13071;ALARCOS Research Group, Department of Technologies and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain 13071

  • Venue:
  • Models in Software Engineering
  • Year:
  • 2009

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Abstract

The main goal of this paper is to show the findings obtained through a meta-analysis study carried out with the data obtained from a family of five controlled experiments performed in academic environments. This family of experiments was carried out to validate empirically two hypotheses applied to UML class diagrams, which investigate 1) The dependence between the structural complexity and size of UML class diagrams on one hand and their cognitive complexity on the other, as well as 2) The dependence between the cognitive complexity of UML class diagrams and their comprehensibility and modifiability. We carried out a meta-analysis, as it allows us to integrate the individual findings obtained from the execution of a family of experiments carried out to test the aforementioned hypotheses. The meta-analysis reveals that the measures related to associations and generalizations have a strong correlation with the cognitive complexity, and that the cognitive complexity has a greater correlation to comprehensibility than to modifiability. These results have implications from the points of view of both modeling and teaching, revealing which UML constructs are most influential when modelers have to comprehend and modify UML class diagrams. In addition, the measures related to associations and generalizations could be used to build prediction models.