Exploring the Differences between Plausible and Correct Patches at Fine-Grained Level
Why this work is in the frame
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Bibliographic record
Abstract
Test-based automated program repair techniques use test cases to validate the correctness of automatically-generated patches. However, insufficient test cases lead to the generation of incorrect patches, i.e., passing all the test cases, however are incorrect. In this work, we present an exploratory study to understand what are the runtime behaviours are being modified by automatically-generated plausible patches, and how such modifications of runtime behaviours are different from those by correct patches. We utilized an off-the-shelf invariant generation tool to infer an abstraction of runtime behaviours and computed the modified runtime behaviours at the abstraction level. Our exploratory study shows that majority of the studied plausible patches (92/96) expose different modifications of runtime behaviours (i.e., captured by the invariant generation tool), compared to correct patches.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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