Bibliographic record
Abstract
Most clone detection techniques have focused on the analysis of source code, however, sometimes stakeholders have access only to compiled code. To address this, some approaches have been developed for finding similarities at the binary level. However, the precise relationships between source-level and binary-level similarities remains unclear: While a compiler will preserve the semantics of the source code in the transformation to an executable, the resulting binary may differ significantly in structure, including the addition and deletion of entities in the source model. Also, compilation sometimes acts as a kind of normalization, transforming syntactically different but semantically similar structures into the same binary-level representation. In this paper, we describe a preliminary study into the effects of the javac Java compiler on the results of clone detection. We use CCFinderX -- which can perform clone detection on sequences of arbitrary tokens -- to find clones in both the source code and the corresponding byte code of four large Java systems. The study shows that source code and byte code clone detection can produce significantly different results, especially for large programs. We report on a few typical examples of differences, and analyze how they are introduced by the compiler. Finally, we discuss the greater significance of this work, and sketch plans for expanded study.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".