ai4st SLR

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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.