Developing Non-Functional Requirements for a Service-Oriented Application Platform
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
The challenges in developing non-functional requirements (NFRs) for an application platform go much beyond those for a single application system. To derive platform NFRs from NFR specifications of different domain applications, requirements analysts must deal with much variation of domain specific NFRs, with different deployment configurations and load conditions, with different NFR related trade-offs, as well as with different terminology and metric definitions. This chapter presents a platform NFR development method that supports dealing with the aforementioned challenges. The presented method offers a goal- and scenario-oriented modeling and analysis technique that supports dealing with qualitative and quantitative NFRs during platform NFR development in an integrated way. The platform NFR development method was used to develop NFRs of a service-oriented application platform for three different application domains in an industrial setting.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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