Mining Temporal Properties of Data Invariants
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
System specifications are important in maintaining program correctness, detecting bugs, understanding systems and guiding test case generation. Often, these specifications are not explicitly written by developers. If we want to use them for analysis, we need to obtain them through other methods; for example, by mining them out of program behavior. Several tools exist to mine data invariants and temporal properties from program traces, but few examine the temporal relationships between data invariants. An example of this kind of relationship would be "the return value of the method isFull? is false until the field size reaches the value capacity". We propose a data-temporal property miner, Quarry, which mines Linear Temporal Logic (LTL) relations of arbitrary length and complexity between Daikon-style data invariants. We infer data invariants from systems using Daikon, recompose these data invariants into sequences, and mine temporal properties over these sequences. Our preliminary results suggest that this method may recover important system properties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.006 | 0.001 |
| 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