Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it