Fast and Flexible Large-Scale Clone Detection with CloneWorks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clone detection in very-large inter-project repositories has numerous applications in software research and development. However, existing tools do not provide the flexibility researchers need to explore this emerging domain. We introduce CloneWorks, a fast and flexible clone detector for large-scale clone detection experiments. CloneWorks gives the user full control over the representation of the source code before clone detection, including easy plug-in of custom source transformation, normalization and filtering logic. The user can then perform targeted clone detection for any type or kind of clone of interest. CloneWorks uses our fast and scalable partitioned partial indexes approach, which can handle any input size on an average workstation using input partitioning. CloneWorks can detect Type-3 clones in an input as large as 250 million lines of code in just four hours on an average workstation, with good recall and precision as measured by our BigCloneBench.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it