Non-Functional Properties in Service Oriented Architecture – A Consumer's Perspective
Bibliographic record
Abstract
Abstract—Information about non-functional properties (NFPs) is rarely explicitly described in Service Oriented Architecture (SOA) services. In particular, there is still no standardized solution addressing what service providers should expose or advertise as NFPs in service descriptions to empower service consumers to decide whether a given service suits best their needs or not. Our goal is to define a catalogue of generic (i.e., domain independent) nonfunctional properties to be considered when service descriptions are developed. This catalogue should be used to better characterize services and enable consumers to perform advanced applications such as NFP-aware service selection. We have identified an initial catalogue of SOA-related NFPs that are relevant from the perspective of consumers (as opposed to providers). Then, we have designed an online survey and invited international SOA experts in many application areas to criticize the relevance and definitions of the proposed NFPs and to enhance this catalogue. After analyzing the survey results to synthesize an improved catalogue, we have validated the new definitions a second time with a subset of the initial participants. We obtained a validated list of 17 NFP definitions for atomic SOA service descriptions relevant from a service consumer’s perspective.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".