From two-way to three-way: domain-specific model differencing and conflict detection.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it