Bug Propagation through Code Cloning: An Empirical Study
Bibliographic record
Abstract
Code clones are defined to be the identical or nearly similar code fragments in a code-base. According to a number of existing studies, code clones are directly related to bugs and inconsistencies in software systems. Code cloning (i.e., creating code clones) is suspected to propagate temporarily hidden bugs from one code fragment to another. However, there is no study on the intensity of bug-propagation through code cloning.In this paper we present our empirical study on bug-propagation through code cloning. We define two clone evolution patterns that reasonably indicate bug propagation through code cloning. We first identify code clones that experienced bug-fix changes by analyzing software evolution history, and then determine which of these code clones evolved following the bug propagation patterns. According to our study on thousands of commits of four open-source subject systems written in Java, up to 33% of the clone fragments that experience bug-fix changes can contain propagated bugs. Around 28.57% of the bug-fixes experienced by the code clones can occur for fixing propagated bugs. We also find that near-miss clones are primarily involved with bug-propagation rather than identical clones. The clone fragments involved with bug propagation are mostly method clones. Bug propagation is more likely to occur in the clone fragments that are created in the same commit operation rather than in different commits. Our findings are important for prioritizing code clones for refactoring and tracking from the perspective of bug propagation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".