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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationSDL 2007
Subtitle of host publicationDesign for Dependable Systems - 13th International SDL Forum, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Print)3540749837, 9783540749837
Publication statusPublished - 2007
Event13th International SDL Forum: Design for Dependable Systems, SDL 2007 - Paris, France
Duration: 18 Sept 200721 Sept 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4745 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International SDL Forum: Design for Dependable Systems, SDL 2007


Dive into the research topics of 'TTCN-3 quality engineering: Using learning techniques to evaluate metric sets'. Together they form a unique fingerprint.

Cite this