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
Regardless of which (model-centric or code-centric) development process is adopted, industrial software production ultimately and necessarily requires the delivery of an executable implementation. It is generally accepted that the quality of such an implementation is of utmost importance. Yet current verification techniques, including software testing, remain problematic. In this paper, we focus on acceptance testing, that is, on the validation of the actual behavior of the implementation under test against the requirements of stakeholder(s). This task must be as objective and automated as possible. Our first goal is to review existing code-based and model-based tools for testing in light of what such an objective and automated approach to acceptance testing entails. Our contention is that the difficulties we identify originate mainly in a lack of traceability between a testable model of the requirements of the stakeholder(s) and the test cases used to validate these requirements. We then investigate whether such traceability is addressed in other relevant specification-based approaches.
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 | 0.001 |
| 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