Analyzing the Impact of Antipatterns on Change-Proneness Using Fine-Grained Source Code Changes
Bibliographic record
Abstract
Antipatterns are poor solutions to design and implementation problems which are claimed to make object oriented systems hard to maintain. Our recent studies showed that classes with antipatterns change more frequently than classes without antipatterns. In this paper, we detail these analyses by taking into account fine-grained source code changes (SCC) extracted from 16 Java open source systems. In particular we investigate: whether classes with antipatterns are more change-prone (in terms of SCC) than classes without, (2) whether the type of antipattern impacts the change-proneness of Java classes, and (3) whether certain types of changes are performed more frequently in classes affected by a certain antipattern. Our results show that: 1) the number of SCC performed in classes affected by antipatterns is statistically greater than the number of SCC performed in classes with no antipattern, 2) classes participating in the three antipatterns Complex Class, Spaghetti Code, and SwissArmyKnife are more change-prone than classes affected by other antipatterns, and 3) certain types of changes are more likely to be performed in classes affected by certain antipatterns, such as API changes are likely to be performed in classes affected by the Complex Class, Spaghetti Code, and SwissArmyKnife antipatterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".