Domain-specific conflict resolution and model merge
Bibliographic record
Abstract
Collaboratively evolving models often necessitate conflict resolution for a cohesive successive version. Resolution can be manual or automated, requiring a sophisticated understanding of change and conflict semantics. While existing version control systems emphasize abstract syntax and fine-grained conflicts, they fall short in catering to user-friendly conflict resolution rule definition for domain experts. Moreover, they visualize conflict resolution concepts based on abstract syntax rather than the concrete syntax of the DSL. To overcome these challenges, we introduced DSMCompare, a tool for comparing domain-specific models, detecting differences, and visualizing conflicts using DSL's concrete syntax. This paper presents enhancements to automate conflict resolution and introduces features for user assistance, including automatically generated domain-specific editors for defining resolution rules. Our evaluation, conducted on the reverse-engineered commit history of various open-source projects, demonstrates DSMCompare's effectiveness 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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".