SwePub
Sök i LIBRIS databas

  Extended search

L773:0730 8566
 

Search: L773:0730 8566 > A classification of...

A classification of code changes and test types dependencies for improving machine learning based test selection

Al Sabbagh, Khaled, 1987 (author)
Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU)
Staron, Miroslaw, 1977 (author)
Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU)
Hebig, Regina, 1984 (author)
Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, Software Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
show more...
Gomes, Francisco, 1987 (author)
Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, Software Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
show less...
 (creator_code:org_t)
2021-08-19
2021
English.
In: SIGPLAN Notices (ACM Special Interest Group on Programming Languages). - New York, NY, USA : ACM. - 0730-8566. ; , s. 40-49
  • Conference paper (peer-reviewed)
Abstract Subject headings
Close  
  • Machine learning has been increasingly used to solve various software engineering tasks. One example of their usage is in regression testing, where a classifier is built using historical code commits to predict which test cases require execution. In this paper, we address the problem of how to link specific code commits to test types to improve the predictive performance of learning models in improving regression testing. We design a dependency taxonomy of the content of committed code and the type of a test case. The taxonomy focuses on two types of code commits: changing memory management and algorithm complexity. We reviewed the literature, surveyed experienced testers from three Swedish-based software companies, and conducted a workshop to develop the taxonomy. The derived taxonomy shows that memory management code should be tested with tests related to performance, load, soak, stress, volume, and capacity; the complexity changes should be tested with the same dedicated tests and maintainability tests. We conclude that this taxonomy can improve the effectiveness of building learning models for regression testing.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Keyword

taxonomy
testing
continuous integration
continuous integration
taxonomy
testing

Publication and Content Type

kon (subject category)
ref (subject category)

Find in a library

To the university's database

Search outside SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view