Bibliographic record
Abstract
Two similar code segments, or clones, form a clone pair within a software system. The changes to the clones over time create a clone evolution history. In this work we study late propagation, a specific pattern of clone evolution. In late propagation, one clone in the clone pair is modified, causing the clone pair to become inconsistent. The code segments are then re-synchronized in a later revision. Existing work has established late propagation as a clone evolution pattern, and suggested that the pattern is related to a high number of faults. In this study we examine the characteristics of late propagation in two long-lived software systems using the Simian and CCFinder clone detection tools. We define 8 types of late propagation and compare them to other forms of clone evolution. Our results not only verify that late propagation is more harmful to software systems, but also establish that some specific cases of late propagations are more harmful than others. Specifically, two cases are most risky: (1) when a clone experiences inconsistent changes and then a re-synchronizing change without any modification to the other clone in a clone pair; and (2) when two clones undergo an inconsistent modification followed by a re-synchronizing change that modifies both the clones in a clone pair.
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 | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".