Bibliographic record
Abstract
"Model Driven Software Development" is a recent trend in development of software-intensive systems. In the Model Driven Software Development process, all knowledge pertaining to the software system to be built is represented in the form of models, in the right formalism(s) and at the right level of abstraction. At the highest level of abstraction, domain models, rather than generic models are used. Although the idea of developing the software system at a higher abstraction level is appealing, many fundamental questions remain unresolved. Many issues such as how to define the syntax and semantics of models, how to represent and store models and how to trace model evolution should be addressed properly. In this thesis, the focus is on model transformations and the open problems related to it. In particular, how to compare models, how to trace model evolution (with as a goal to undo and redo model changes), how to deal with meta-model evolution, and ultimately with semantics evolution are explored. For each issue, we analyze problems, and propose some solutions. We use small case studies to make issues more concrete. All case studies are developed in AToM3 (A Tool for Multi-formalism and Meta-Modeling), developed in the Modeling, Simulation and Design Lab (MSDL) in the School of Computer Science of McGill University.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".