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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".