Towards Web Collaborative Modelling for the User Requirements Notation Using Eclipse Che and Theia IDE
Bibliographic record
Abstract
Collaborative modelling has become a necessity when developing a complex system or in a team of modellers with a diverse set of expertise. Textual notations have a long history in software engineering because of their fast editing style, simple usage, and scalability. Therefore, we propose a novel collaborative modelling framework for the graphical User Requirements Notation (URN) which we call tColab. It uses the text-based TGRL (Textual Goal-oriented Requirement Language) to build URN goal models and then automatically generates corresponding graphical models. This framework is based on the architecture of Eclipse Che and Theia. On one side, Theia provides support for LSP (Language Server Protocol) so that textual models can be built and their corresponding graphical models can be generated in a browser IDE (Integrated Development Environment). On the other hand, Eclipse Che adds support for collaboration where multiple modellers can contribute to building the textual models in an online collaborative manner. This initiative aims to replace the jUCMNAV tool, which is the most comprehensive URN modelling tool to date but only supports a single user.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".