You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Tech. Report: Similarity Function Evaluation

Publication Type:

Report - MRTC

Publisher:

Mälardalen Real-Time Research Centre, Mälardalen University

ISRN:

MDH-MRTC-315/2017-1-SE


Abstract

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.

Bibtex

@techreport{Flemstrom4713,
author = {Daniel Flemstr{\"o}m},
title = {Tech. Report: Similarity Function Evaluation},
month = {April},
year = {2017},
publisher = {M{\"a}lardalen Real-Time Research Centre, M{\"a}lardalen University},
url = {http://www.es.mdh.se/publications/4713-}
}