An automatic approach to identify class evolution discontinuities
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
When a software system evolves, features are added, removed and changed. Moreover, refactoring activities are periodically performed to improve the software internal structure. A class may be replaced by another, two classes can be merged, or a class may be split in two others. As a consequence, it may not be possible to trace software features between a release and another. When studying software evolution, we should be able to trace a class lifetime even when it disappears because it is replaced by a similar one, split or merged. Such a capability is also essential to perform impact analysis. This work proposes an automatic approach, inspired on vector space information retrieval, to identify class evolution discontinuities and, therefore, cases of possible refactoring. The approach has been applied to identify refactorings performed over 40 releases of a Java open source domain name server. Almost all the refactorings found were actually performed in the analyzed system, thus indicating the helpfulness of the approach and of the developed tool.
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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 | 0.001 |
| 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