Peer Review #1 of "Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk (v0.1)"
Bibliographic record
Abstract
Background.Test resources are usually limited and therefore it is often not possible to completely test an application before a release.To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions.However, defect prediction tends to low precision in cross-project prediction scenarios. Aims.We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial".We expect that characteristics of such methods might be project-independent, so that our approach could improve crossproject predictions.Method.We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk.We conduct an empirical study to assess our approach with six Java opensource projects containing precise fault data at the method level. Results.Our results show that inverse defect prediction can identify approx.32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods.In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions.Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.
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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 | 0.025 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.190 | 0.133 |
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".