From two-way to three-way: domain-specific model differencing and conflict detection.
Bibliographic record
Abstract
In collaborative work, developers evolve their models in parallel, leading to substantial differences and conflicts.To better consolidate these changes, developers need to understand the differences in terms of syntax and semantics of the models.Despite myriad efforts, the existing version control systems and model comparison tools focus on the generic models, are hardly adaptable to a domain-specific language (DSL), and primarily present syntactical changes to the developer.Furthermore, they report differences and conflicts of domain-specific models based on their abstract syntax instead of the concrete syntax of the DSL.To address these issues, we previously introduced DSMCompare to detect fine-grained and semantic differences between pairs of model versions and present the changes in the concrete syntax of the DSL.In this paper, we have further enhanced our practice by considering a three-way model comparison, typical in the context of version control systems.DSMCompare can now report differences coming from either version as well as conflicts.To detect semantic differences and conflicts, our approach relies on the DSL engineer specifying semantic differencing patterns in an editor adapted to the DSL.To evaluate DSMCompare, we reverse-engineered the commit history of several open-source projects where Java-based code refactoring changes occur.We show that DSMCompare effectively finds these semantic differences and conflicts with high accuracy.
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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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".