Use case and task models : formal unification and integrated development methodology
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
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 this shortcoming, we propose an integrated development methodology for use case and task models. Our methodology serves as a blueprint for practitioners to derive an iterative and incremental development process according to which the two artifacts are successively enhanced in a stepwise and integrated manner. With each step, it is verified that the resulting model is a valid refinement of its source model. For this purpose we define a suite of refinement relations for use case and/or task models and provide automated tool support. The integrated development methodology is based on a formal framework, which defines a two-step mapping from a particular use case or task model notation to a common semantic domain. This two-step mapping results in a semantic framework that can be more easily validated, reused and extended. The intermediate semantic domains have been carefully chosen by taking into consideration the intrinsic characteristics of use case and task models. We selected sets of partially ordered sets (posets) and nondeterministic finite state machines (nFSMs) as semantic domains, supporting a true concurrent and interleaving model of concurrency, respectively. During the course of our research, we also defined DSRG-style use case models and Extended CIT task models as improvements to their respective state-of-the-art counterparts. Each improvement has been carefully selected to ensure that the intent and nature of each model is preserved. In order to show that the set of posets semantics and the nFSM semantics coincide, we established a formal correspondence between the two semantics and prove that they are trace equivalent
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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