A Z specification of use cases: a preliminary report
Bibliographic record
Abstract
The use case concept is a tool for capturing the requirements of a system. A single use case describes a subset of a system's functionality in terms of the interactions between the system and a set of users or actors. A use case is initiated by a particular user, and serves the purpose of delivering some meaningful unit of work, service, or value to the initiator. When capturing requirements, a use case views the system as a black box. Due to their popularity, the concept of use cases has been abused to some extent, and been applied to specifying the "requirements" of all sorts of things, such as those of a subsystem of the system architecture. Cockburn (1997) acknowledges 18 different definitions of use cases. This has created a great deal of confusion and a need for clear definitions. Just what is a "use case"? We seek to answer that question, by providing a specification of a use case and its related concepts using the Z formalism.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".