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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".