Mapping Code Smells and Refactorings Accurately: Insights from an Empirical Study
Bibliographic record
Abstract
Background: Code smells indicate underlying quality issues that negatively impact software maintainability. Refactoring is a common way to improve code quality by restructuring it, often removing these code smells. While many recommendations exist on how to refactor code smells, we do not fully understand how developers how they are removed by developers in the real world. Aim: In this study, we aim to investigate the evolution of code smells and the impact of applied refactoring techniques. Method: Our study addresses this gap by investigating both implementation and design smells and the refactoring techniques developers use to remove them. We also explore how often code smells are removed using established refactoring techniques. We analyzed 212,664 commits from 87 open-source Java projects using both automated tools and manual review to understand the relationship between code smells and refactoring. Results: Our key findings include: a) Extract method refactoring is most effective at fixing multiple smell types, b) Most applied refactorings do not remove code smells, c) About 82% of removed code smells are “dangling” i.e., they are removed without a matching refactoring technique, and d) Design smells typically last longer in codebases than implementation smells. Conclusions: This research improves our understanding of the interplay between code smells and refactoring effectiveness. Our results can help researchers develop better tools and guide software engineers in making their refactoring processes more efficient.
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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 | 0.035 | 0.283 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".