Accurate method and variable tracking in commit history
Bibliographic record
Abstract
Tracking program elements in the commit history of a project is essential for supporting various software maintenance, comprehension and evolution tasks. Accuracy is of paramount importance for the adoption of program element tracking tools by developers and researchers. To this end, we propose CodeTracker, a refactoring-aware tool that can generate the commit change history for method and variable declarations with a very high accuracy. More specifically, CodeTracker has 99.9% precision and recall in method tracking, surpassing the previous state-of-the-art tool, CodeShovel, with a comparable execution time. CodeTracker is the first tool of its kind that can track the change history of variables with 99.7% precision and 99.8% recall. To evaluate its accuracy in variable tracking, we extended the oracle created by Grund et al. for the evaluation of CodeShovel, with the complete change history of all 1345 variables and parameters declared in the 200 methods comprising the Grund et al. oracle. We make our tool and extended oracle publicly available to enable the replication of our experiments and facilitate future research on program element tracking techniques.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".