On Extracting Tests from a Testable Model in the Context of Domain Engineering
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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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".