A Bibliometric Analysis of AI-Driven Adaptive and Predictive Testing in Agile Software Quality Assurance
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into Agile Software Quality Assurance (SQA) is rapidly advancing, particularly in adaptive and predictive testing. This paper presents a bibliometric analysis of publications retrieved from Dimensions.ai using VOSviewer to map research trends, collaboration patterns, influential contributors, key sources, and thematic evolution in AI-driven testing within Agile software development. The collaboration and country-level analyses reveal that early research in this domain was predominantly led by the United States, China, Canada, and leading European nations such as Germany and Sweden. Over time, contributions from Asian and Middle Eastern countries, including India, Pakistan, and Saudi Arabia have increased significantly. This trend indicates a transition from a Westerncentric research focus towards a more globally distributed and collaborative ecosystem in AI-driven software engineering. Key institutional hubs include Monash University, Blekinge Institute of Technology, and the University of Oulu, supported by prominent authors such as Michael Felderer, John Grundy, Davide Taibi, and David Lo. The Empirical Software Engineering and the Journal of Systems and Software exhibit the highest research activity, whereas IEEE Transactions on Software Engineering demonstrates strong influence through high citation rates. Highly cited works in Proceedings of the IEEE and Nature Methods indicate exceptional impact, with ACM Computing Surveys and Expert Systems with Applications reflecting a balance between research volume and citation quality. The study highlights a diversifying and globalizing research landscape, offering valuable insights into emerging directions for AI-driven adaptive and predictive testing in Agile environments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.098 | 0.444 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".