SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:research.chalmers.se:508327f0-f8c2-44e5-bd50-eefbc6ec0ef6"
 

Sökning: id:"swepub:oai:research.chalmers.se:508327f0-f8c2-44e5-bd50-eefbc6ec0ef6" > A Taxonomy of Infor...

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

Ramírez, Aurora (författare)
Universidad de Córdoba
Feldt, Robert, 1972 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Romero, José Raúl (författare)
Universidad de Córdoba
 (creator_code:org_t)
2023-02-13
2023
Engelska.
Ingår i: ACM Transactions on Software Engineering and Methodology. - : Association for Computing Machinery (ACM). - 1049-331X .- 1557-7392. ; 32:1
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Most software companies have extensive test suites and re-run parts of them continuously to ensure that recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This article analyses two decades of TCP research and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to system under test code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (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)

Nyckelord

Regression testing
taxonomy
test case prioritisation
industry
machine learning

Publikations- och innehållstyp

art (ämneskategori)
ref (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför 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 Stäng

Kopiera och spara länken för att återkomma till aktuell vy