On synthesizing test cases in symbolic real-time testing
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
Abstract Test synthesis (or test generation) can be described as follows: from a formal specification of an implementation under test ( IUT ), and from a test purpose describing behaviors to be tested, the aim is to synthesize test cases to be executed in order to check whether the IUT conforms to its formal specification, while trying to control the IUT so that it satisfies the test purpose. In this paper, we study the synthesis of test cases for symbolic real-time systems. By symbolic, we mean that the specification of the IUT contains variables and parameters. And by realtime, we mean that the specification of the IUT contains timing constraints. Our method combines and generalizes two testing methods presented in previous work, namely: 1) a method for synthesizing test cases for (non-symbolic) real-time systems, and 2) a method for synthesizing test cases for (non-real-time) symbolic systems.
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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 | 0.001 | 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 | 0.002 | 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