Using Imprecise Test Oracles Modelled by FSM
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
Test oracles are usually used to evaluate the behaviors of systems under test to reveal faults. In a typical conformance testing scenario, a test oracle is a deterministic finite state machine (FSM). However, uncertainty occurring in the design of an oracle may result in a set of potential candidate oracles which can compactly be represented by a nondeterministic FSM thus modelling an imprecise test oracle. In the context of testing deterministic systems, such an oracle should ideally be reduced to a precise, i.e., deterministic oracle. We elaborate two scenarios for dealing with imprecise test oracles that involve a domain expert playing the role of an "ultimate" oracle. In the first scenario, the expert chooses a right oracle by inspecting the generated tests differentiating all potential precise oracles which can be derived from a given imprecise one. In the second scenario, the expert evaluates tests one by one while they are generated, and the imprecise oracle is iteratively reduced until a single precise oracle remains, if at all.
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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 | 0.000 |
| 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