Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Cluster-based test scheduling strategies using semantic relationships between test specifications
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-8724-9049
Mälardalen University, Sweden.
University of Innsbruck, Austria.
Mälardalen University, Sweden.
Show others and affiliations
2018 (English)Conference paper, Published paper (Other academic)
Abstract [en]

One of the challenging issues in improving the test efficiency isthat of achieving a balance between testing goals and testing resources.Test execution scheduling is one way of saving time andbudget, where a set of test cases are grouped and tested at thesame time. To have an optimal test execution schedule, all relatedinformation of a test case (e.g. execution time, functionality to betested, dependency and similarity with other test cases) need tobe analyzed. Test scheduling problem becomes more complicatedat high-level testing, such as integration testing and especially inmanual testing procedure. Test specifications are generally writtenin natural text by humans and usually contain ambiguity anduncertainty. Therefore, analyzing a test specification demands astrong learning algorithm. In this position paper, we propose anatural language processing-based approach that, given test specificationsat the integration level, allows automatic detection oftest cases semantic dependencies. The proposed approach utilizesthe Doc2Vec algorithm and converts each test case into a vectorin n-dimensional space. These vectors are then grouped using theHDBSCAN clustering algorithm into semantic clusters. Finally, aset of cluster-based test scheduling strategies are proposed for execution.The proposed approach has been applied in a sub-systemfrom the railway domain by analyzing an ongoing testing projectat Bombardier Transportation AB, Sweden.

Place, publisher, year, edition, pages
2018. p. 1-4
Keywords [en]
clustering, dependency, Doc2Vec, HDBSCAN, NLP, software testing, test optimization
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-34878DOI: 10.1145/3195538.3195540Scopus ID: 2-s2.0-85051238162ISBN: 978-1-4503-5749-4 (print)OAI: oai:DiVA.org:ri-34878DiVA, id: diva2:1240488
Conference
Proceedings of the 5th International Workshop on Requirements Engineering and Testing. Gothenburg, Sweden
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-03-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Tahvili, SaharSaadatmand, MehrdadBohlin, Markus

Search in DiVA

By author/editor
Tahvili, SaharSaadatmand, MehrdadBohlin, Markus
By organisation
SICS
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 17 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.9