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
Code coverage is one of the main metrics to measure the adequacy of a test case/suite. It has been studied a lot in academia and used even more in industry. However, a test case may cover a piece of code (no matter what coverage metric is being used) but miss its faults. In this paper, we studied several existing and standard control and data flow coverage criteria on a set of developer-written fault-revealing test cases from several releases of five open source projects. We found that a) basic criteria such as statement coverage is very weak (detecting only 10% of the faults), b) combining several control-flow coverage together is better than the strongest criterion alone (28% vs. 19%), c) a basic data-flow coverage can detect many undetected faults (79% of the undetected faults by control-flow coverage can be detected by a basic def/use pair coverage), and d) on average 15% of the faults may not be detected by any of the standard control and data-flow coverage criteria. Classification of the undetected faults showed that they are mostly to do with specification (missing logic).
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 | 0.001 |
| 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