Ten years of JDeodorant: Lessons learned from the hunt for smells
Bibliographic record
Abstract
Deodorants are different from perfumes, because they are applied directly on body and by killing bacteria they reduce odours and offer a refreshing fragrance. That was our goal when we first thought about "bad smells" in code: to develop techniques for effectively identifying and removing (i.e., deodorizing) code smells from object-oriented software. JDeodorant encompasses a number of techniques for suggesting and automatically applying refactoring opportunities on Java source code, in a way that requires limited effort on behalf of the developer. In contrast to other approaches that rely on generic strategies that can be adapted to various smells, JDeodorant adopts ad-hoc strategies for each smell considering the particular characteristics of the underlying design or code problem. In this retrospective paper, we discuss the impact of JDeodorant over the last ten years and a number of tools and techniques that have been developed for a similar purpose which either compare their results with JDeodorant or have built on top of JDeodorant. Finally, we discuss the empirical findings from a number of studies that employed JDeodorant to extract their datasets.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".