An empirical study of the factors affecting co-change frequency of cloned code
Bibliographic record
Abstract
Code clones are duplicated code fragments that are copied to re-use functionality and speed up development. However, due to the duplicate nature of code clones, inconsistent updates can lead to defects in software system. We extend the existing studies on the inconsistent co-change characteristics, by investigating further factors that affect clone evolution. We study the effect of development cycles, the number of developers, method names similarity and code complexity. Our empirical study includes six industrial software systems to determine if the observations are statistically significant. We discover that one way to improve maintenance of code clones is to decrease code complexity. We find that increased code complexity leads to a decrease in co-change, which can lead to software defects. Likewise, we find that method name similarity is an important factor on co-change frequency of cloned code. From development cycles point of view, we observe that co-change frequency of cloned code does not change significantly from early to later and from development to defect fixing cycles. As a result, we suggest assigning a higher priority for early refactoring (i.e., within the first six months) of all cloned methods with infrequent co-change focusing on clone classes with low similarity in method names and high code complexity.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".