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 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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".