SPCP-Miner: A tool for mining code clones that are important for refactoring or tracking
Bibliographic record
Abstract
Code cloning has both positive and negative impacts on software maintenance and evolution. Focusing on the issues related to code cloning, researchers suggest to manage code clones through refactoring and tracking. However, it is impractical to refactor or track all clones in a software system. Thus, it is essential to identify which clones are important for refactoring and also, which clones are important for tracking. In this paper, we present a tool called SPCP-Miner which is the pioneer one to automatically identify and rank the important refactoring as well as important tracking candidates from the whole set of clones in a software system. SPCP-Miner implements the existing techniques that we used to conduct a large scale empirical study on SPCP clones (i.e., the clones that evolved following a Similarity Preserving Change Pattern called SPCP). We believe that SPCP-Miner can help us in better management of code clones by suggesting important clones for refactoring or tracking.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".