Modeling Enhanced Scenarios for Automated Instrumentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a resurgence of research in model-based testing, especially in the automated generation of test cases from abstract models. However this work largely remains theoretical: industrial adoption is low. This is partly due to the dominance of state-based approaches that often rely on global states that are problematic with respect to scalability and traceability. Developers and testers alike significantly prefer the intuitive nature, traceability and user-friendliness of scenarios, to the semantics of formal approaches. Proposals for scenario-driven testing exist but, as is the case for the vast majority of existing work on model-based testing, there is a considerable gap between the generated test cases and their corresponding IUT instrumentation. It is this problem we address here. In this paper we focus on modeling responsibilities and scenarios within a scenario-driven testing framework that generates fully-instrumented test cases. Our work proceeds from the scenario contracts proposed by Nebut et al.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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