A delta‐oriented approach to support the safe reuse of black‐box code rewriters
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
Abstract Large‐scale corrective and perfective maintenance is often automated thanks to rewriting rules using tools such as Python2to3 , Spoon , or Coccinelle . Such tools consider these rules as black‐boxes and compose multiple rules by chaining them: giving the output of a given rewriting rule as input to the next one. It is up to the developer to identify the right order (if it exists) among all the different rules to yield the right program. In this paper, we define a formal model compatible with the black‐box assumption that reifies the modifications (Δs) made by each rule. Leveraging these Δs, we propose a way to safely compose multiple rules when applied to the same program by (a) ensuring the isolated application of the different rules and (b) identifying unexpected behaviors that were silently ignored before. We assess this approach on two large‐scale case studies: (a) identifying conflicts in the Linux source‐code automated maintenance and (b) fixing energy antipatterns existing in Android applications available on GitHub.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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