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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".