Scaling classical clone detection tools for ultra-large datasets: an exploratory study
Bibliographic record
Abstract
Abstract—Detecting clones from large datasets is an interesting research topic for a number of reasons. However, building scalable clone detection tools is challenging and it is often impossible to use existing state of the art tools for such large datasets. In this research we have investigated the use of our Shuffling Framework for scaling classical clone detection tools to ultra large datasets. This framework achieves scalability on standard hardware by partitioning the dataset and shuffling the partitions over a number of detection rounds. This approach does not require modification to the subject tools, which allows their individual strengths and precisions to be captured at an acceptable loss of recall. In our study, we explored the performance and applicability of our framework for six clone detection tools. The clones found during our experiment were used to comment on the cloning habits of the global Java open-source development community. Index Terms—Clone detection, scalability, large dataset I.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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".