Reasoning about Global Clones: Scalable Semantic Clone Detection
Bibliographic record
Abstract
The Semantic Web is slowly transforming the Web as we know it into a machine understandable pool of information that can be consumed and reasoned about by various clients. Source code is no exception to this trend and various communities have proposed standards to share code as linked data. With the availability of large amounts of open source code published in publicly accessible repositories, the introduction of massive horizontal scaling frameworks, and cloud computing infrastructures, a new era of software mining across information silos is reshaping the software engineering landscape. Given these technological advances, analyzing code at a global scale, across systems, projects and organizational boundaries, becomes feasible. In this paper, we introduce a clone detection algorithm and its implementation that can scale to such large global datasets, by modeling clones using description logic and applying a horizontal scaling Semantic Web reasoner. We demonstrate how our simple feature vector that only uses control statements, data types and method calls, can yield results similar to other popular clone detection tools. Our approach does not only allow us to reliably identify clones in a global context. By using a semantic reasoner, it also allows us to expand clone detection to a new class of semantic clones. We have compared our algorithm to some of the leading clone detection tools (DECKARD, CCFinder, JCD, and Simian) in order to validate our approach and show the differences in detected clones and performance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".