Dispersion of changes in cloned and non-cloned code
Bibliographic record
Abstract
Abstract—Currently, the impacts of clones in software main-tenance activities are being investigated by different researchers in different ways. Comparative stability analysis of cloned and non-cloned regions of a subject system is a well-known way of measuring the impacts where the hypothesis is that, the more a region is stable the less it is harmful for maintenance. Each of the existing stability measurement methods lacks to address one important characteristic, dispersion, of the changes happening in the cloned and non-cloned regions of software systems. Change dispersion of a particular region quantifies the extent to which the changes are scattered over that region. The intuition is that, more dispersed changes require more efforts to be spent in the maintenance phase. Measurement of Dispersion requires the extraction of method genealogies. In this paper, we have measured the dis-persions of changes in cloned and non-cloned regions of several subject systems using a concurrent and robust framework for method genealogy extraction. We implemented the framework on Actor Architecture platform which facilitates coarse grained parallellism with asynchronous message passing capabilities. Our experimental results with 12 open-source subject systems written in three different programming languages (Java, C and C#) using two clone detection tools suggest that, the changes in cloned regions are more dispersed than the changes in non-cloned regions. Also, Type-3 clones exhibit more dispersion as compared to the Type-1 and Type-2 clones. The subject systems written in Java and C show higher dispersions as well as increased maintenance efforts as compared to the subject systems written in C#.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".