SimCad: An extensible and faster clone detection tool for large scale software systems
Bibliographic record
Abstract
Code cloning is an inevitable phenomenon in evolution of software systems. To reduce the harmful effects of clones in software evolution, they need to be identified correctly as well in a time efficient way. There might be various types of clones in a software system. Earlier research shows detection of near-miss clones in large datasets appears to be costly in terms of time and memory. Among the clone detection tools available in practice, not very many of them are found effective in that regard. In this paper we present a standalone clone detection tool SimCad. It is based on a highly scalable and faster clone detection algorithm designed to detect both exact and near-miss clones in large-scale software systems. One of the potential aspects of SimCad is that its clone detection function is made more portable by packaging it into a library called SimLib. Thus, SimLib now can be used as an off-the-shelf clone detection library that can be easily integrated into other applications that are designed to work based on detected clones. For example, a standalone tool or an Integrated Development Environment (IDE) plugin can use SimLib for realtime clone detection while providing its own services like clone visualization and/or clone management functionalities. We hope that both researchers and developers would enjoy and utilize the benefit of using these tools in different aspects of detection and management of clones in software.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".