Recovering commit dependencies for selective code integration in software product lines
Bibliographic record
Abstract
In software product lines, multiple products of a software product family, share source code of common components. New features added to the common components of a software product family, are integrated into products following a selective code integration process. Selective code integration is a process in which developers pick the commits (i.e., code changes) related to a feature from one code branch and integrate them into another code branch. Developers often manually link the commits to the features to enable the selective integration of features. In current practice, not all dependent commits are always linked to features and developers might miss the unlinked commits during selective code integration. In this paper, we propose two grouping approaches that identify dependencies among commits and create groups of dependent commits that need to be integrated as a whole into a code branch. Our first approach is automatic and the other is developer-guided. Through a case study on data derived from a product line of mobile software applications, we show that our approaches can help to reduce by up to 94% integration failures caused by missing commit dependencies.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".