Omkar2118/datasets huggingface dataset:
ai4st SLR
Full name: Research on AI for Software Testing Research, 2020-2025

To check the validity of the ai4st ontology, an adapted, lightweight systematic literature review (SLR) was conducted to analyse related research. This SLR protocol was followed:

Review title: Initial SLR on the application of the ai4st taxonomy.

Objectives of the review: 

   - To test the validity of the ai4st taxonomy with an initial research selection.

   - To determine a classification of this initial research selection.

Research questions

   -  RQ1: Which standardized terms are being used in \textit{ai4st} related research?

   - RQ2: Which alternative terms are being used in \textit{ai4st} related research?

   - RQ3: Are the \textit{ai4st} taxonomy dimensions useful to classify the pre-selected research?

Database: Research papers from the conference proceedings of the latest International Conference on Software Engineering, ICSE 2025 in Ottawa, Canada and its co-located conferences and workshops; and referenced papers for backward snowballing the research. Forward snowballing was in this case unnecessary, as ICSE 2025 represented the most recent research publications at that time. A complementary search of the IEEE and ACM digital libraries has added further software testing and AI-related research papers published between 2020 and 2025.

Inclusion criteria

   - Peer-reviewed original research.

   - Online available.

   - Research on AI for ST.

Exclusion criteria: 

   - Meta-research such as evaluations, benchmarking, comparisons, surveys, taxonomies, roadmaps.

   - Testing of software-based systems like IoT, cloud, vehicle, etc.

   - Research on ST for AI.

   - Posters and tutorials.

Selection process:

   - Title and abstract screening for the pre-selection of unique research candidates by use of the concept map resulting from the stc ontology to identify ST-related research, and the concept map resulting from the ai4st dimensions 'AI type' to identify AI related research in the ST-related subset of research.

   - Full text review and assessment of the research contributions for the final selection of unique research.

Tools: 

   - Online research libraries, including dblp, ACM DL, IEEE Xplore, and Google Scholar to identify related work; and

   - Python for text analysis and post-processing of finally selected research, supported by MS Visual Studio, Google AI Studio, and LibreOffice

Synthesis process: 

   - Review of the new and synonym candidate terms for inclusion into the stc ontology.

   - Classification of the selected research for inclusion into the stc ontology.

Introducing paper: Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey