A Framework for Studying Clones In Large Software Systems
Bibliographic record
Abstract
Clones are code segments that have been created by copying-and-pasting from other code segments. Clones occur often in large software systems. It is reported that 5 to 50% of the source code of a large software system is cloned. A major challenge when studying code cloning in large software systems is handling the large amount of clone candidates produced by leading edge clone detection tools. For example, the CCFinder, clone detection tool, produces over 7 million pairs of clone candidates for the Linux kernel (which consists of over 4MLOC). Moreover, the output of clone detection tools grows rapidly as a software system evolves. Researchers and developers need tools to help them study the large amount of clone data in order to better understand the clone phenomena in large systems. In this paper, we propose a data mining framework to help researchers cope with the large amount of data produced by clone detection tools. We propose techniques to reduce, abstract and highlight the most interesting data produced by clone detection tools. Our framework also introduces a visualization tool which allows users to query and explore clone data at various abstraction levels. We demonstrate our framework on a case study of the clone phenomena in the Linux kernel.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.004 |
| 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".