Towards convenient management of software clone codes in practice: an integrated approach
Bibliographic record
Abstract
Software code cloning is inevitable during software development and unmanaged cloning practice can create substantial problems for software maintenance and evolution. Current research in the area software clones includes, but is not limited to: finding ways to manage clones; gaining more control over clone generation; and, studying clone evolution and its effects on the evolution of software. In this study, we investigate tools and techniques for detecting, managing, and understanding the evolution of clones, as well as design a convenient tool to make those techniques available to a developer's software development environment. Towards the goal of promoting the practical use of code clone research and to provide better support for managing clones in software systems, we first developed SimEclipse: a clone-aware software development platform, and then, using the tool, we performed a study to investigate the usefulness of using a number clone based technologies in an integrated platform rather than using those discretely. Finally, a small scale user study is performed to evaluate SimEclipse's effectiveness, usability and information management with respect to some pre-defined clone management activities. We believe that both researchers and developers would enjoy and utilize the benefits of using SimEclipse for different aspects of code clone research as well as for managing cloned code in software systems.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".