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Tech. Report: Similarity Function Evaluation
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8096-3592
2017 (English)Report (Other academic)
Abstract [en]

This report presents details on and an in-depth evaluation of a similarity function used for detecting similar test steps in manual test cases, written in natural language. Using an industrial data set of 65 000 test steps, we show that even though the similarity function builds on standard functions from the open source data base Postgres, it is capable of finding similarities in parity of what the state of the art suggests. Rather few miss classifications were found. We also show that by fine tuning the function, the number of clusters of similar can be reduced by 13%. Manual inspection further shows that there is potential to reduce the set of clusters even more.

Place, publisher, year, edition, pages
Västerås, Sweden: Mälardalen Real-Time Research Centre, Mälardalen University , 2017.
Series
MRTC Reports, ISSN 1404-3041
Keyword [en]
Software Engineering, Sofware Testing, Similarity Detection
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35489ISRN: MDH-MRTC-315/2017-1-SEOAI: oai:DiVA.org:mdh-35489DiVA: diva2:1104486
Available from: 2017-06-01 Created: 2017-06-01 Last updated: 2017-06-01Bibliographically approved

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http://www.es.mdh.se/pdf_publications/4713.pdf

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Citation style
  • apa
  • harvard1
  • ieee
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Output format
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