NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the effectiveness of a new language- specific parser-based but lightweight clone detection approach. Exploiting a novel application of a source transformation system, the method accurately finds near-miss clones using an efficient text line comparison technique. The transformation system assists the method in three ways. First, using agile parsing it provides user-specified flexible pretty- printing to remove noise, standardize formatting and break program statements into parts such that potential changes can be detected as simple linewise text differences. Second, it provides efficient flexible extraction of potential clones to be compared using island grammars and agile parsing to select granularities and enumerate potential clones. Third, using transformation rules it provides flexible code normalization to allow for local editing differences between similar code segments and filtering out of uninteresting parts of potential clones. In this paper we introduce the theory and practice of the framework and demonstrate its use in finding function clones in C code. Early experiments indicate that the method is capable of finding near-miss clones with high precision and recall, and with reasonable performance.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it