Towards a mutation-based automatic framework for evaluating code clone detection tools
Bibliographic record
Abstract
In the last decade, a great many code clone detection tools have been proposed. Such a large number of tools calls for a quantitative comparison, and there have been several attempts to empirically evaluate and compare many of the state-of-the-art tools. However, a recent study shows that there are several factors that could influence the the validity of the results of such comparisons. In order to overcome the effects of such factors (at least in part), in this student poster paper we outline a mutation-based controlled frame-work for evaluating clone detection tools using edit-based mutation operators that model cloning actions. While the framework is not yet completely implemented and as yet we do not have experimental data, we anticipate that such a framework will provide a useful contribution to the community by providing a more solid objective foundation for tool evaluation.
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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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".