Towards Conflict-Free Collaborative Modelling using VS Code Extensions
Bibliographic record
Abstract
Model-Driven Engineering (MDE) advocates the use of models and their transformations, to better understand software systems and to increase the degree of automation across the software development process. However, with the increasing complexity of modern software systems, distributed development teams, and increasing time pressure for developing these systems, there is a need to collaborate more quickly when building and analyzing models. Furthermore, the COVID-19 pandemic has forced classroom-based software projects to organizational-level software systems to rely on virtual (web-based) collaborative development environments. Therefore, real-time collaborative modelling remains no longer an option but becomes a necessity for MDE too. In our previous work, we introduce a framework, tColab, which uses Eclipse Che workspaces to enable web-based collaborative modelling. However, with real-time collaboration, modelling conflicts can arise and their resolution goes beyond what is possible with the collaborative environment facilitated by an Eclipse Che workspace. In this paper, we extend our tColab framework for building modelling language editors as Visual Studio (VS) Code extensions. These VS Code extensions are well supported by widely used platforms such as VS Code IDE, Eclipse Theia IDE, and the Eclipse Che platform. Furthermore, to facilitate real-time collaboration using these VS Code extensions and to enable conflict-free modelling, we explore two possible solutions – the VS Code Live Share extension and the Teletype CRDTs (conflict-free replicated data types) library. Finally, we provide a prototypical VS Code extension for the TGRL (Textual Goal-oriented Requirement Language) as a proof-of-concept of our extended framework and demonstrate conflict-free collaborative modelling for TGRL using the Live Share extension.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".