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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".