Using WADL Specifications to Develop and Maintain REST Client Applications
Bibliographic record
Abstract
Service orientation is one of the most popular paradigms for developing modular distributed software systems. In spite of the substantial research effort dedicated to the development of methods and tools to support SOAP-based service-oriented application development, in practice, RESTful services have surpassed SOAP-based services in popularity and adoption, primarily due to the simplicity of their invocation. However, poor adoption of REST specification standards and lack of systematic development tools have given rise to many, more or less compliant, variants of the Restful style constraints, which undermine the evolvability and interoperability of these systems. In this paper, we describe a tool that supports the systematization of RESTful application development, through the use of semi-automatically constructed WADL interface specifications, without compromising the ease of the overall practice. We illustrate the use and advantages of our tool on real-world REST APIs. Additionally, we comment on how REST APIs are documented, especially in comparison to the auto-generated WADLs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".