The Norwegian SISU Project: History and Long-term Impact of an Early MDD Effort
Bibliographic record
Abstract
At the 15th System Analysis and Modelling Conference (SAM) in October 2023, a panel session was dedicated to the discussion of the past, present, and the future of Model-Driven Development (MDD). The session focused on the themes history, impact, lessons learned, and barriers to the adoption of MDD. In the context of history and impact, amongst others, the results of the Norwegian national R&D project “Supporting Integrated System Development” (SISU) were discussed, as well as recent development approaches akin to MDD. The panelists agreed that the quality of the systems produced within SISU was usually very high, since the used modeling concepts match reality well and made the models therefore easier to comprehend. Nevertheless, the adoption of SDL in the member companies did not progress as expected after project completion. This makes SISU a typical example of the circumstance that MDD has not developed as successfully as was assumed 30 years ago. The discussion of the panelists on barriers to MDD revealed key challenges in two perspectives: users' experience and tools' support. Besides some lessons learned, this paper presents a number of recommendations that might help to address the mentioned challenges leading towards a more prominent use of MDD in software engineering in the future.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".