Experimenting with Category Partition's 1-Way and 2-Way Test Selection Criteria
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
The Category Partition (CP) black-box testing method has shown to be effective in a number of situations. There is however little support for automating its use and little is known about the cost effectiveness of its associated selection criteria. In this paper, we report on a tool to automatically create test frames, i.e., test case specifications, for three wellknown criteria associated with the CP method, including the application of 1-way and 2-way interactions. We then report on the cost, in terms of number of test cases, and the effectiveness, in terms of mutation score, of adequate test suites for these criteria. The main lesson learnt is that, in addition to the intuition that the CP specification does impact effectiveness, a significant part of the effectiveness is also due to the test input selection procedure for test frames.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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