Bibliographic record
Abstract
Whitening the testing of service-oriented applications can provide service consumers confidence on how well an application has been tested. However, to protect business interests of service providers and to prevent information leakage, the implementation details of services are usually invisible to service consumers. This makes it challenging to determine the test coverage of a service composition as a whole and design test cases effectively. To address this problem, we propose an approach to whiten the testing of service compositions based on events exposed by services. By deriving event interfaces to explore only necessary test coverage information from service implementations, our approach allows service consumers to determine test coverage based on selected events exposed by services at runtime without releasing the service implementation details. We also develop an approach to design test cases effectively based on event interfaces concerning both effectiveness and information leakage. The experimental results show that our approach outperforms existing testing approaches for service compositions with up to 49 percent more test coverage and an up to 24 percent higher fault-detection rate. Moreover, our solution can trade off effectiveness, efficiency, and information leakage for test case generation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".