Automated instrumentation of contracts and scenarios for requirements validation in .net
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
During the development of an object-oriented reactive system, scenarios (such as UML's use cases) may be used for the elicitation of functional and non-functional requirements. The contribution of this paper is the overview of a framework for the specification of a testable requirements model and the automated instrumentation of this model into an implementation in order to validate the model's requirements against this implementation. Our testable model takes the form of contracts and is grounded in the notions of scenarios and responsibilities. More precisely, the validation of the requirements of this model depends on a user binding elements of contracts to actual procedures within a candidate implementation, (that also supplies test data). Once this is done, these requirements are validated against an execution. This validation consists in the invocation of both static and dynamic checks, the matching of scenarios, and the capture and evaluation of metrics for an execution. Metric evaluation allows our framework and testable model to also consider non-functional requirements.
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