Domain-specific conflict resolution and model merge
Bibliographic record
Abstract
• Domain-specific version control system: DSMCompare • Domain-specific model merging • Curated labeled dataset for model merging • Comparative evaluation with EMFCompare and Git • User study validating DSMCompare Software developers often collaborate by contributing to different branches in a version control system. However, merging the changes from the different branches often leads to conflicts, and resolving these conflicts is a tedious task. This challenge is exacerbated when the software to merge are domain-specific models, since they follow a graph-like structure rather than linear text. DSMCompare is a tool for comparing domain-specific models, detecting differences, and visualizing conflicts using the concrete syntax of the domain-specific language. In this paper, we enhance DSMCompare with conflict resolution capabilities to reduce the effort of merging model versions. Our evaluation demonstrates that DSMCompare is effective in achieving highly accurate automatic conflict resolution with minimal manual intervention. A user study further validates the tool, revealing a significant decrease in resolution time coupled with higher accuracy and user satisfaction.
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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 | 0.001 | 0.000 |
| 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".