An Initial Approach to Reuse Non-Functional Requirements Knowledge
Bibliographic record
Abstract
Non-Functional Requirements (NFR) can be seen as qualities that software should deliver to cope with the stakeholders' demands. NFRs are fuzzy in nature and hence hard to identify. Despite the fact that both developers and users may value NFRs, they frequently do not identify the need for an NFR. Even when an NFR is identified as required, possible solutions to implement this NFR may be hard to fig- ure out. Furthermore, interdependencies among NFRs may implicate that a solution for one NFR may, at the same time, bring synergy to one NFR while conflicting with another. One approach to deal with that is to use Softgoal Interdependency Graphs (SIG) to capture knowledge describing alternatives to implement NFRs. We have ob- tained empirical evidence that using catalogues can help eliciting NFRs despite the fact that catalogues do not scale too well. To address this question, we have investi- gated the use of ontologies and semantic web techniques to represent SIGs in a ma- chine readable format. We have produced a tool (NDR) that starts to use these con- cepts. In its current form, the NDR tool only allows very basic queries done manual- ly. The NDR tool is part of the NDR framework which will facilitate the reuse of NFR knowledge on Alternatives to incorporate NFRs into the design of target sys-
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".