A Comparative Study of Software Bugs in Micro-clones and Regular Code Clones
Bibliographic record
Abstract
Reusing a code fragment through copy/pasting, also known as code cloning, is a common practice during software development and maintenance. Most of the existing studies on code clones ignore micro-clones where the size of a micro-clone fragment can be 1 to 4 LOC. In this paper we compare the bug-proneness of micro-clones with that of regular code clones. From thousands of revisions of six diverse open-source subject systems written in three languages (C, C#, and Java), we identify and investigate both regular and micro-clones that are associated with reported bugs.Our experiment reveals that percentage of changed code fragments due to bug-fix commits is significantly higher in micro-clones than regular clones. The number of consistent changes due to bug-fix commits is significantly higher in micro-clones than regular clones. We also observe that significantly higher percentage of files get affected by bug-fix commits in micro-clones than regular clones. Finally, we found that percentage of severe bugs is significantly higher in micro-clones than regular clones. We perform Mann-Whitney-Wilcoxon (MWW) test to evaluate the statistical significance level of our experimental results. Our findings imply that micro-clones should be emphasized during clone management and software maintenance.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".