Driving Non-Functional Requirements to Use Cases and Scenarios
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
Today, companies are continuously changing and improving their business strategies. As a consequence, stakeholders are demanding more flexible and complex software to be built. To handle this complexity, conceptual models have to describe aspects beyond entities and activities. Recent research has pointed out that dealing with goals in order to capture intentions associated with complex situations is a major aspect to handle this new reality [14]. Non-Functional Requirements are a particular class of these goals that has to be dealt with since the early stages of software development. Therefore, expressing these NFRs in use cases and scenarios models is a must. In this work we show a strategy to drive elicited NFR towards use cases and scenarios that reflect the functional requirements of the software. We tested our proposal through two case studies and the results suggest that our strategy can help developers to deal with complex conceptual models and might result in a more complete software specification and thus, to a shorter time-to-the-market.
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 | 0.002 |
| 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