Evaluating clone detection tools with BigCloneBench
Why this work is in the frame
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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. In this paper, we use our big data clone benchmark, BigCloneBench, to evaluate the recall of ten clone detection tools. BigCloneBench is a collection of eight million validated clones within IJaDataset-2.0, a big data software repository containing 25,000 open-source Java systems. BigCloneBench contains both intra-project and inter-project clones of the four primary clone types. We use this benchmark to evaluate the recall of the tools per clone type and across the entire range of clone syntactical similarity. We evaluate the tools for both single-system and cross-project detection scenarios. Using multiple clone-matching metrics, we evaluate the quality of the tools' reporting of the benchmark clones with respect to refactoring and automatic clone analysis use-cases. We compare these real-world results against our Mutation and Injection Framework, a synthetic benchmark, to reveal deeper understanding of the tools. We found that the tools have strong recall for Type-1 and Type-2 clones, as well as Type-3 clones with high syntactical similarity. The tools have weaker detection of clones with lower syntactical similarity.
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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 | 0.000 | 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