Insight into a method co-change pattern to identify highly coupled methods: An empirical study
Bibliographic record
Abstract
In this paper, we describe an empirical study of a unique method co-change pattern that has the potential to pinpoint design deficiency in a software system. We automatically identify this pattern by inspecting the method co-change history using reasonable constraints on method association rules. We also investigate the effect of code clones on the method co-changes identified according to the pattern, because there is a common intuition that clone fragments from the same clone class often require corresponding changes to ensure they remain consistent with each other. According to our in-depth investigation on hundreds of revisions of seven open-source software systems considering three types of clones (Type 1, Type 2, Type 3), our identified pattern helps us detect methods that are logically coupled with multiple other methods and that exhibit a significantly higher modification frequency than other methods. We call the methods detected by the pattern MMCGs (Methods appearing in Multiple Commit Groups) considering the pattern semantic. MMCGs can be considered as the candidates for restructuring in order to minimize coupling as well as to reduce the change-proneness of a software system. According to our observation, code clones have a significant effect on method co-changes as well as on MMCGs. We believe that clone refactoring can help us minimize evolutionary coupling among methods.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".