Bibliographic record
Abstract
The service-oriented architecture paradigm prescribes the development of systems through the composition of services, i.e., network-accessible components, specified by (and invoked through) their WSDL interface descriptions. Systems thus developed need to be aware of changes in, and evolve with, their constituent services. To support this coevolution process, we have developed WSDarwin, a toolkit that facilitates both providers and clients in the evolution of service-oriented systems. In this work, we focus primarily on the comparison of service-interface versions, in order to precisely recognize their differences, and the adaptation of client applications. We propose a lightweight model to represent service interfaces, an efficient and accurate comparison method whose output can be seamlessly consumed by the adaptation process, a classification of changes in service interfaces based on their impact on client applications and, finally, a generic adaptation algorithm that can be applied for any type of change and on any client regardless of the implementation technology. We demonstrate this part of the WSDarwin toolkit on a client application invoking several versions from the Amazon EC2 web service and we report on the challenges we faced.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".