Semantic Slicing of Software Version Histories (T)
Bibliographic record
Abstract
Software developers often need to transfer func-tionality, e.g., a set of commits implementing a new feature or a bug fix, from one branch of a configuration management system to another. That can be a challenging task as the existing configuration management tools lack support for matching high-level semantic functionality with low-level version histories. The developer thus has to either manually identify the exact set of semantically-related commits implementing the functionality of interest or sequentially port a specific subset of the change history, "inheriting" additional, unwanted functionality. In this paper, we tackle this problem by providing automated support for identifying the set of semantically-related commits implementing a particular functionality, which is defined by a set of tests. We refer to our approach, CSLICER, as semantic slicing of version histories. We formally define the semantic slicing problem, provide an algorithm for identifying a set of commits that constitute a slice, and instantiate it in a specific implementation for Java projects managed in Git. We evaluate the correctness and effectiveness of our approach on a set of open-source software repositories. We show that it allows to identify subsets of change histories that maintain the functionality of interest but are substantially smaller than the original ones.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".