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
Next-Generation Software Testing: AI-Powered Test Automation
University of Genoa, Italy.
University Carlos III Madrid, Spain.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0002-8569-2290
UCL, London, England..
2025 (English)In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 42, no 4, p. 25-33Article in journal, Editorial material (Other academic) Published
Abstract [en]

Software testing has long been a cornerstone of software development, ensuring the delivery of high-quality, reliable, and secure systems. However, as software systems grow in complexity and scale, traditional testing methods might fail to address their needs. Manual testing is often time-consuming and error-prone. Automated testing, while beneficial for efficiency, repeatability, and excellent coverage, presents challenges such as high initial costs and maintenance overhead. The promise of AI-powered test automation lies in its ability to address some of the most pressing challenges in software testing.

Place, publisher, year, edition, pages
IEEE Computer Society, 2025. Vol. 42, no 4, p. 25-33
Keywords [en]
Special issues and sections, Software testing, Artificial intelligence, Automation, Next generation networking
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-28089DOI: 10.1109/MS.2025.3559194ISI: 001502548100006Scopus ID: 2-s2.0-105018622594OAI: oai:DiVA.org:bth-28089DiVA, id: diva2:1968896
Available from: 2025-06-13 Created: 2025-06-13 Last updated: 2025-10-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Nass, Michel

Search in DiVA

By author/editor
Nass, Michel
By organisation
Department of Software Engineering
In the same journal
IEEE Software
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 313 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