Clones and Macro-Co-Changes
Bibliographic record
Abstract
Ideally, any change that modifies the similar parts of a cloned code snippet should be propagated to all its duplicates. In practice however, consistent propagation of changes in clones does not always happen. Current evidence indicates that clone families have a 50% chance of having consistent changes. This paper measures cloning and co-changes at file level as a proxy to assess the frequency of consistent changes. Given that changes to a clone group are not necessarily propagated in the same commit transaction (i.e., late propagations), our analysis uses macro co-changes instead of the traditional definition of co-changes. Macro changes group bursts of changes that are closer among themselves than to other changes, regardless of author or message. Then, macro co-changes are sets of files that change in the same macro changes. Each cloned file is tagged depending on whether any of the files with which it macro co-changes is cloned with it (during the macro change) or not. Contrary to previous results, we discovered that most of the cloned files macro co-change only with files with which they share clones. Thus providing evidence that macro changes are appropriate to study the conjecture of clones requiring co-changes, and indicating that consistent changes might be the norm in cloned code.
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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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".