BigCloneEval: A Clone Detection Tool Evaluation Framework with BigCloneBench
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
Many clone detection tools have been proposed in the literature. However, our knowledge of their performance in real software systems is limited, particularly their recall. We previously introduced our BigCloneBench, a big clone benchmark of over 8 million clones within a large inter-project Java repository containing 25,000 open-source Java systems. In this paper we present BigCloneEval, a framework for evaluating clone detection tools with BigCloneBench. BigCloneEval makes it very easy for clone detection researchers to evaluate and compare clone detection tools. It automates the execution and evaluation of clone detection tools against the reference clones of BigCloneBench, and summarizes recall performance from a variety of perspectives, including per clone type, and per syntactical similarity regions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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