TTCN-3 quality engineering: using learning techniques to evaluate metric sets

  • Authors:

  • Edith Werner;Jens Grabowski;Helmut Neukirchen;Nils Röttger;Stephan Waack;Benjamin Zeiss

  • Affiliations:

  • Institute for Informatics, University of Göttingen, Göttingen, Germany;Institute for Informatics, University of Göttingen, Göttingen, Germany;Institute for Informatics, University of Göttingen, Göttingen, Germany;Institute for Informatics, University of Göttingen, Göttingen, Germany;Institute for Informatics, University of Göttingen, Göttingen, Germany;Institute for Informatics, University of Göttingen, Göttingen, Germany

  • Venue:
  • SDL'07 Proceedings of the 13th international SDL Forum conference on Design for dependable systems
  • Year:
  • 2007

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Abstract

Software metrics are an essential means to assess software quality. For the assessment of software quality, typically sets of complementing metrics are used since individual metrics cover only isolated quality aspects rather than a quality characteristic as a whole. The choice of the metrics within such metric sets, however, is non-trivial. Metrics may intuitively appear to be complementing, but they often are in fact non-orthogonal, i.e. the information they provide may overlap to some extent. In the past, such redundant metrics have been identified, for example, by statistical correlation methods. This paper presents, based on machine learning, a novel approach to minimise sets of metrics by identifying and removing metrics which have little effect on the overall quality assessment. To demonstrate the application of this approach, results from an experiment are provided. In this experiment, a set of metrics that is used to assess the analysability of test suites that are specified using the Testing and Test Control Notation (TTCN-3) is investigated.