Bibliographic record
Abstract
Many clone detection tools and techniques have been introduced in the literature, and these tools have been used to manage clones and study their effects on software maintenance and evolution. However, the performance of these modern tools is not well known, especially recall. In this paper, we evaluate and compare the recall of eleven modern clone detection tools using four benchmark frameworks, including: (1) Bellon's Framework, (2) our modification to Bellon's Framework to improve the accuracy of its clone matching metrics, (3) Murakamki et al.'s extension of Bellon's Framework which adds type 3 gap awareness to the framework, and (4) our Mutation and Injection Framework. Bellon's Framework uses a curated corpus of manually validated clones detected by tools contemporary to 2002. In contrast, our Mutation and Injection Framework synthesizes a corpus of artificial clones using a cloning taxonomy produced in 2009. While still very popular in the clone community, there is some concern that Bellon's corpus may not be accurate for modern clone detection tools. We investigate the accuracy of the frameworks by (1) checking for anomalies in their results, (2) checking for agreement between the frameworks, and (3) checking for agreement with our expectations of these tools. Our expectations are researched and flexible. While expectations may contain inaccuracies, they are valuable for identifying possible inaccuracies in a benchmark. We find anomalies in the results of Bellon's Framework, and disagreement with both our expectations and the Mutation Framework. We conclude that Bellon's Framework may not be accurate for modern tools, and that an update of its corpus with clones detected by the modern tools is warranted. The results of the Mutation Framework agree with our expectations in most cases. We suggest that it is a good solution for evaluating modern tools.
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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 | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".