Refactoring Practice: How it is and How it Should be Supported - An Eclipse Case Study
Bibliographic record
Abstract
Refactoring is an important activity in the evolutionary development of object-oriented software systems. Yet, several questions about the practice of refactoring remain unanswered, such as what fraction of code modifications are refactorings and what are the most frequent types of refactorings. To gain some insight in this matter, we conducted a detailed case study on the structural evolution of Eclipse, an integrated-development environment (IDE) and a plugin-based framework. Our study indicates that: 1) about 70% of structural changes may be due to refactorings; 2) for about 60% of these changes, the references to the affected entities in a component-based application can be automatically updated by a refactoring-migration tool if the relevant information of refactored components can be gathered through the refactoring engine; and 3) state-of-the-art IDEs, such as Eclipse, support only a subset of commonly applied low-level refactorings and lack support for more complex ones, which are also frequent. Based on our findings, we draw some conclusions on high-level design requirements for a refactoring-based development environment
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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 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".