TTCN-3 Quality Engineering: Using Learning Techniques to Evaluate Metric Sets

Edith Werner, Jens Grabowski, Helmut Neukirchen, Nils Röttger, Stephan Waack, Benjamin Zeiß

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.
Document Type: 
Articles in Conference Proceedings
Booktitle: 
Proceedings of 13th System Design Language Forum (SDL Forum 2007)
Series: 
SDL'07
Address: 
Paris, France
Publisher: 
Springer-Verlag
Pages: 
56-70
Month: 
9
Year: 
2007
2011 © Software Engineering For Distributed Systems Group

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