The Problems of Large-Scale Refactoring: Learning from Eclipse RCP
Bibliographic record
Abstract
Investing in planning big refactoring changes prior to implementing them has been promoted as a positive practice that guarantees a high quality code change process. However, there is no empirical evidence of the potential risks that may degrade the quality of such a change process, even if changes are planned in advance. This paper identifies and categorizes potential risks based on a real-world case of large-scale refactoring: that which produced the Eclipse Rich Client Platform (RCP). This case is interesting because it is industrially relevant and three publicly available data sources exist for it. We analyzed these data sources in retrospect, and found that when expert engineers were mapping out changes, (1) they were uncertain about what the code does, (2) they were unclear about what it affects if the code is changed, and (3) they misunderstood each other when changes were described. If such lack of knowledge, and miscommunication among the expert engineers were not resolved via peer discussions prior to applying the changes, we anticipate that such changes would result in a later wrong decision or action. We also found that (4) many small changes are enacted in the code for which the sequencing matters and that were poorly-communicated while planning. Thus, these changes cannot be reviewed by other engineers until after they are fully implemented. We hypothesize that such lack of knowledge and miscommunication would adversely affect the quality of a largescale refactoring task, especially when the complexity of the task increases and the level of expertise decreases.
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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 | 0.027 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".