Bibliographic record
Abstract
Refactoring is a common software development practice and many simple refactorings can be performed automatically by tools. Identifier renaming is a widely performed refactoring activity. With tool support, rename refactorings can rely on the program structure to ensure correctness of the code transformation. Unfortunately, the textual references to the renamed identifier present in the unstructured comment text cannot be formally detected through the syntax of the language, and are thus fragile with respect to identifier renaming. We designed a new rule-based approach to detect fragile comments. Our approach, called Fraco, takes into account the type of identifier, its morphology, the scope of the identifier and the location of comments. We evaluated the approach by comparing its precision and recall against hand-annotated benchmarks created for six target Java systems, and compared the results against the performance of Eclipse's automated in-comment identifier replacement feature. Fraco performed with near-optimal precision and recall on most components of our evaluation data set, and generally outperformed the baseline Eclipse feature. As part of our evaluation, we also noted that more than half of the total number of identifiers in our data set had fragile comments after renaming, which further motivates the need for research on automatic comment refactoring.
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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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".