Building measure-based prediction models for UML class diagram maintainability

  • Authors:

  • Marcela Genero;Esperanza Manso;Aaron Visaggio;Gerardo Canfora;Mario Piattini

  • Affiliations:

  • ALARCOS Research Group, Department of Technologies and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain 13071;GIRO Research Group, Department of Computer Science, University of Valladolid, Valladolid, Spain 47011;RCOST--Research Centre on Software Technology, University of Sannio, Benevento, Italy 82100;RCOST--Research Centre on Software Technology, University of Sannio, Benevento, Italy 82100;ALARCOS Research Group, Department of Technologies and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain 13071

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2007

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Abstract

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.