WSDATool: A Novel Web Service Developer Assistance Tool Using a New Complementary Service Publishing Method
Bibliographic record
Abstract
Web service technology (WST) is a service-oriented architecture implementation framework that makes designing component-based internet applications possible. At present, many providers offer their services as web services. Current WST suffers from the lack of an integrated tool to assist web service developers. In WST, the services are published publicly, and their descriptions are stored in service directories. These descriptions contain valuable information about the work of different software teams throughout the world. However, with the increasing number of web services, searching for services is difficult and time-consuming. Furthermore, in current service directories, there is a little knowledge about the services, and extraction of useful information to be utilised by developers is not easy. In this paper, in order to increase the knowledge of what is available in service directories, a structure is presented by interlinking WST entities by using some defined semantic relations. The proposed structure provides a framework and a tool named WSDATool to develop new web services using information from published services or to refine current published web service descriptions. In experiments, services designed with the assistance of the WSDATool are more coherent and well designed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".