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Building measure-based prediction models for UML class diagram maintainability

Authors
  • Genero, Marcela1
  • Manso, Esperanza2
  • Visaggio, Aaron3
  • Canfora, Gerardo3
  • Piattini, Mario1
  • 1 University of Castilla-La Mancha, ALARCOS Research Group, Department of Technologies and Information Systems, Paseo de la Universidad, 4, Ciudad Real, 13071, Spain , Ciudad Real (Spain)
  • 2 University of Valladolid, GIRO Research Group, Department of Computer Science, Campus Miguel Delibes, E.T.I.C., Valladolid, 47011, Spain , Valladolid (Spain)
  • 3 University of Sannio, RCOST—Research Centre on Software Technology, Pal. Ex Poste, viale Traiano, Benevento, 82100, Italy , Benevento (Italy)
Type
Published Article
Journal
Empirical Software Engineering
Publisher
Springer US
Publication Date
Mar 21, 2007
Volume
12
Issue
5
Pages
517–549
Identifiers
DOI: 10.1007/s10664-007-9038-4
Source
Springer Nature
Keywords
License
Yellow

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.

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