Improving the detection accuracy of evolutionary coupling by measuring change correspondence
Bibliographic record
Abstract
If two or more program entities change together (i.e., co-change) frequently (i.e., in many commits) during software evolution, it is likely that the entities are related and we say that the entities are showing evolutionary coupling. Association rules have been used to express evolutionary coupling and two related measures, support and confidence, have been used to measure the strength of coupling among the co-changed entities. However, an association rule relies only on the number of times the entities have co-changed. It does not analyze whether the changes are corresponding and whether the entities are really related. As a result, association rule often reports false positives and also, ignores important coupling among the infrequently co-changed entities. Focusing on this issue we propose to calculate a new measure, change correspondence, blending the idea of concept location in a code-base to determine whether the changes to the co-changed entities are corresponding and thus, whether they are really related. Our preliminary investigation result on four subject systems written in two programming languages shows that change correspondence has the potential to accurately determine whether two entities are related even if they co-changed infrequently. Thus, we believe that our new measure will help us improve the detection accuracy of evolutionary coupling.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".