Non-functional requirements: from elicitation to modelling languages
Bibliographic record
Abstract
Although Non-Functional Requirements (NFRs) have been present in many software development methods, they have been presented as a second or even third class type of requirement, frequently hidden inside notes and therefore, frequently neglected or forgotten. Surprisingly, despite the fact that non-functional requirements arc among the most expensive and difficult to deal with there are still few works that focus on NFRs as first class requirements. Although these Works have brought a contribution on how to represent and deal with NFRs, two aspects remain not sufficiently explored: how to elicit NFRs and how to merge these NFRs with conceptual models. Our work aims at filling this gap, proposing a strategy to elicit NFRs and to integrate them into conceptual models We focus our attention on conceptual models expressed using UML, and therefore, we propose extensions to UML such that NFRs can be expressed. More precisely, we will show how to integrate NFRs to the Class, Sequence and Collaboration Diagrams. We will also show how Use Cases and Scenarios can be adapted to deal with NFRs. This work was validated by three case studies and their results suggest that by using our proposal we can improve the quality of UML 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.
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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".