On Extracting Tests from a Testable Model in the Context of Domain Engineering
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
Software testing is the traditional way to verify the functionality of a given software system against its requirements. In domain engineering, these requirements consist of variabilities and commonalities observed in a domain and captured in a domain model [5]. We remark that the latter may be used to obtain an elaborate design; however tests cannot be derived from it. This observation proceeds from the fact that testing techniques relevant to single-system engineering cannot deal with the variability intrinsic to a domain. Therefore, in the context of domain engineering, we claim that there is a need for a new modeling approach enabling domain testing. We have proposed elsewhere [1, 3, 4] a testable [2] domain model (based on the domain requirements) that takes the form of generative contracts. In this paper, we present a test extraction technique applicable to this testable model. This technique generates tests for validating behavioural aspects of an implemented member of the domain against that member's requirements. That is, upon selecting a specific member to test, the variability of domain tests is eliminated, resulting in member- specific tests, which are to be bound to artefacts of that member's corresponding implementation in order to obtain executable tests for this member. A case study on a domain-specific testable model will illustrate the steps of our proposed test extraction technique.
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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 | 0.000 | 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 | 0.001 | 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