Investigating Near-Miss Micro-Clones in Evolving Software
Bibliographic record
Abstract
Code clones are the same or nearly similar code fragments in a software system's code-base. While the existing studies have extensively studied regular code clones in software systems, micro-clones have been mostly ignored. Although an existing study investigated consistent changes in exact micro-clones, near-miss micro-clones have never been investigated. In our study, we investigate the importance of near-miss micro-clones in software evolution and maintenance by automatically detecting and analyzing the consistent updates that they experienced during the whole period of evolution of our subject systems. We compare the consistent co-change tendency of near-miss micro-clones with that of exact micro-clones and regular code clones. According to our investigation on thousands of revisions of six open-source subject systems written in two different programming languages, near-miss micro-clones have a significantly higher tendency of experiencing consistent updates compared to exact micro-clones and regular (both exact and near-miss) code clones. Consistent updates in near-miss micro-clones have a high tendency of being related with bug-fixes. Moreover, the percentage of commit operations where near-miss micro-clones experience consistent updates is considerably higher than that of regular clones and exact micro-clones. We finally observe that near-miss micro-clones staying in close proximity to each other have a high tendency of experiencing consistent updates. Our research implies that near-miss micro-clones should be considered equally important as of regular clones and exact micro-clones when making clone management decisions.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".