@InProceedings{Schieferdecker2025c,
  author       = {Schieferdecker, Ina K},
  booktitle    = {International Conference on Formal Methods for Industrial Critical Systems},
  title        = {Navigating the growing field of research on AI for software testing--the taxonomy for AI-augmented software testing and an ontology-driven literature survey},
  doi          = {10.1007/978-3-032-00942-5_1},
  organization = {Springer},
  pages        = {3--20},
  abstract     = {In industry, software testing is the primary method to verify
and validate the functionality, performance, security, usability, and so on,
of software-based systems. Test automation has gained increasing atten-
tion in industry over the last decade, following decades of intense research
into test automation and model-based testing. However, designing, devel-
oping, maintaining and evolving test automation is a considerable effort.
Meanwhile, AI’s breakthroughs in many engineering fields are opening
up new perspectives for software testing, for both manual and automated
testing. This paper reviews recent research on AI augmentation in soft-
ware test automation, from no automation to full automation. It also
discusses new forms of testing made possible by AI. Based on this, the
newly developed taxonomy, ai4st, is presented and used to classify recent
research and identify open research questions.},
  award        = {Keynote},
  file         = {:2025/Schieferdecker2025c - Navigating the Growing Field of Research on AI for Software Testing the Taxonomy for AI Augmented Software Testing and an Ontology Driven Literature Survey.pdf:PDF},
  type         = {conference},
  year         = {2025}
}