A multidimensional empirical study on refactoring activity
Bibliographic record
Abstract
In this paper we present an empirical study on the refactoring activity in three well-known projects. We have studied five research questions that explore the different types of refactorings applied to different types of sources, the individual contribution of team members on refactoring activities, the alignment of refactoring activity with release dates and testing periods, and the motivation behind the applied refactorings. The studied projects have a history of 12, 7, and 6 years, respectively. We have found that there is very little variation in the types of refactorings applied on test code, since the majority of the refactorings focus on the reorganization and renaming of classes. Additionally, we have identified that the refactoring decision making and application is often performed by individual refactoring managers. We have found a strong alignment between refactoring activity and release dates. Moreover, we found that the development teams apply a considerable amount of refactorings during testing periods. Finally, we have also found that in addition to code smell resolution the main drivers for applying refactorings are the introduction of extension points, and the resolution of backward compatibility issues.
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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 | 0.016 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".