Estimating Object-Relational Database Understandability Using Structural Metrics

  • Coral Calero
  • Houari A. Sahraoui
  • Mario Piattini
  • Hakim Lounis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2113)


New Object-Relational Database Management Systems (ORDBMSs) are replacing existing relational ones. In spite of the high expressiveness, application systems built upon ORDBMS are more complex and difficult to maintain due to the mixing of two paradigms, the relational and the objectoriented. This paper describes a suite of metrics for measuring different aspects of an object-relational database. An empirical validation of the usefulness of the proposed metrics in estimating the understandability of an object-relational schema is given. The analysis procedure comprises the use of two techniques: C4.5, a machine learning algorithm, and RoC, a robust Bayesian classifier. The results demonstrate that a subset of the proposed measures is relevant for the estimation of the understandability.


Structural Metrics Machine Learning Algorithm Bayesian Classifier Software Maintenance Shared Classis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Coral Calero
    • 1
  • Houari A. Sahraoui
    • 2
  • Mario Piattini
    • 1
  • Hakim Lounis
    • 3
  1. 1.Dep. InformáticaUniversidad de Castilla-La Mancha Ronda CalatravaCiudad RealSpain
  2. 2.Dep. d’Informatique et de Recherche OpérationnelleUniversité de MontréalMontréalCanada
  3. 3.Département d’informatiqueUniversité du Québec a MontréalMontréalCanada

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