Partial order semantics for use case and task models
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
Abstract Use case models are the specification medium of choice for functional requirements, while task models are employed to capture User Interface (UI) requirements and design information. In current practice, both entities are treated independently and are often developed by different teams, which have their own philosophies and lifecycles. This lack of integration is problematic and often results in inconsistent functional and UI design specifications causing duplication of effort while increasing the maintenance overhead. To address these shortcomings, we propose a formal semantic framework for the integrated development of use case and task models. The semantic mapping is defined in a two step manner from a particular use case or task model notation to the common semantic domain of sets of partially ordered sets . This two-step mapping results in a semantic framework that can be more easily reused and extended. The intermediate semantic domains have been carefully chosen by taking into consideration the intrinsic characteristics of use case and task models. As a concrete example, we provide a semantics for our own DSRG use case formalism and an extended version of ConcurTaskTrees, one of the most popular task model notations. Furthermore, we use the common semantic model to formally define a set of refinement relations for use case and task models.
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 | 0.001 |
| 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 | 0.001 |
| 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 it