A Survey of Model-Driven Testing Techniques
Why this work is in the frame
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Bibliographic record
Abstract
The model-driven approach to software development has not only changed the way software systems are built and maintained but also the way they are tested. For such systems, a model-based testing approach is much recommended since it is aligned with the new model-driven development paradigm that favors models over code with the objective being to reduce time to market while improving product quality. There has been a noticeable increase in the number of model-driven testing techniques in recent years. Although these techniques have a common objective they tend to vary significantly in their design. In this paper, we discuss the model-driven testing techniques presented in 15 different studies. We compare these techniques according to specific criteria including the modeling language used to represent the system design artifacts, the ability to automatically generate test cases, the testing target, and tool support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it