Towards Conflict-Free Collaborative Modelling using VS Code Extensions
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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