DECOR : détection et correction des défauts dans les systèmes orientés objet
Bibliographic record
Abstract
Code and design smells are implementation and design problems that come from ''poor'' recurring design choices. They may hinder development and maintenance of systems by making them hard for software engineers to change and evolve. A semi-automatic detection and correction are thus key factors to ease the maintenance and evolution stages. Techniques and tools have been proposed in the literature both for the detection and correction of smells. The detection techniques proposed consist mainly in defining rules for detecting smells and applying them to the source code of a system. As for the correction techniques, they consist in applying automatically refactorings in the source code of the system analysed to restructure it and correct the smells. However, software engineers have to identify manually how the system must be restructured. Thus, it is not possible to correct directly and automatically the detected smells. This problem is due to the fact that the detection and the correction of smells are treated independently. Thus, we propose DECOR, a method that encompasses and defines all steps necessary for the detection and correction of code and design smells. This method allows software engineers to specify detection rules at a high level of abstraction and to obtain automatically suggestions for code restructuring. We apply and validate our method on open-source object-oriented systems to show that our method allows a precise detection and a suitable correction of smells.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".