Modeling and Analyzing Non-Functional Requirements in Service Oriented Architecture with the User Requirements Notation
Bibliographic record
Abstract
Non-functional properties (NFPs) represent an important facet of service descriptions, especially in a Service Oriented Architecture. Yet, they are seldom explicitly described, and their use in service selection and composition is still limited. This chapter presents the User Requirements Notation (URN) as a means to model and analyze functional and non-functional service requirements. Aspect-oriented extensions to URN (AoURN) enable the modeling and modularization of different concerns, including non-functional requirements, which can crosscut services or service components. The chapter also proposes a taxonomy of NFPs used to annotate services and service compositions modeled with AoURN. These annotations enable the specification of quantitative non-functional values for services, guide service selection, and support the computation of the NFP (e.g., the quality of service) of their composition. This approach is illustrated with a simple yet realistic composite service (BookItWell), with an emphasis on four types of NFPs, namely service cost, response time, reliability, and availability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".