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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".