BHive: Towards Behaviour-Driven Development Supported by B-Method
Bibliographic record
Abstract
Behaviour-Driven Development (BDD) is an "outside-in" approach to software development built upon semi-formal mediums for specifying the behaviour of a system as it would be observed externally. Through the representation of a system as a collection of user stories and scenarios using BDD's notation, practitioners automate acceptance tests using examples of desired behaviour for the envisioned system. A formal model created in concert with BDD tests would provide valuable insight into test validity and enhance the visibility of the problem domain. This work called BHive builds upon the formal underpinnings of BDD scenarios by mapping their "Given," "When," and "Then" statements to "Precondition," "Command," and "Postcondition" constructs as introduced by Floyd-Hoare logic. We posit that this mapping allows for a B-Method representation to be created and that such a model is useful for exploring system behaviour and exposing gaps in requirements. We also outline extensions to BDD tooling required for the described integration and present benefits of the BHive approach to integrating formalism within a BDD project.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".