Understanding the evolution of Type-3 clones: An exploratory study
Bibliographic record
Abstract
Understanding the evolution of clones is important both for understanding the maintenance implications of clones and building a robust clone management system. To this end, researchers have already conducted a number of studies to analyze the evolution of clones, mostly focusing on Type-1 and Type-2 clones. However, although there are a significant number of Type-3 clones in software systems, we know a little how they actually evolve. In this paper, we perform an exploratory study on the evolution of Type-1, Type-2, and Type-3 clones in six open source software systems written in two different programming languages and compare the result with a previous study to better understand the evolution of Type-3 clones. Our results show that although Type-3 clones are more likely to change inconsistently, the absolute number of consistently changed Type-3 clone classes is higher than that of Type-1 and Type-2. Type-3 clone classes also have a lifespan similar to that of Type-1 and Type-2 clones. In addition, a considerable number of Type-1 and Type-2 clones convert into Type-3 clones during evolution. Therefore, it is important to manage type-3 clones properly to limit their negative impact. However, various automated clone management techniques such as notifying developers about clone changes or linked editing should be chosen carefully due to the inconsistent nature of Type-3 clones.
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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 | 0.001 |
| 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".