Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Automated functional dependency detection between test cases using Doc2Vec and Clustering
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
2019 (English)In: Proceedings - 2019 IEEE International Conference on Artificial Intelligence Testing, AITest 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 19-26Conference paper, Published paper (Refereed)
Abstract [en]

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 19-26
Keywords [en]
Clustering Doc2Vec, FCM, HDBSCAN, Paragraph Vectors, Software Testing, Test Case Dependency, Artificial intelligence, Cost effectiveness, Semantics, Testing, Bombardier Transportation, Classification methods, Functional dependency, Random under samplings, Test case, Train control systems, Clustering algorithms
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39269DOI: 10.1109/AITest.2019.00-13Scopus ID: 2-s2.0-85067096441ISBN: 9781728104928 (print)OAI: oai:DiVA.org:ri-39269DiVA, id: diva2:1334740
Conference
1st IEEE International Conference on Artificial Intelligence Testing, AITest 2019, 4 April 2019 through 9 April 2019
Note

Funding details: 20130085, 20160139; Funding details: VINNOVA, MegaM@RT2; Funding text 1: ECSEL & VINNOVA (through projects MegaM@RT2 & TESTOMAT) and the Swedish Knowledge Foundation (through the projects TOCSYC (20130085) and TestMine (20160139)) have supported this work.

Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-07-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Tahvili, SaharBohlin, Markus

Search in DiVA

By author/editor
Tahvili, SaharBohlin, Markus
By organisation
SICS
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 1 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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. 2.35.7