Enabling Model-Driven Software Development Tools for the Internet of Things
Bibliographic record
Abstract
The heterogeneity and complexity of Internet of Things (IoT) applications present new challenges to the software development process. Model-Driven Software Development (MDSD) is increasingly being recognized as a key paradigm in tackling many of these challenges, as evident by the emergence of a significant number of MDSD frameworks targeting IoT in the past couple of years. At the heart of IoT applications are embedded and realtime systems, a domain where model-driven development is well-established and many existing tools have a proven track record. Unfortunately, only a handful of these tools support out-of-the-box integration with the IoT. In this work, we discuss the different design and implementation decisions for enabling existing actor-oriented MDSD tools for the IoT. Moreover, we propose an integration approach based on the use of proxy actors and system interfaces. The approach offers seamless and flexible integration of external IoT devices into the user's model. We implement and evaluate our approach using the MDSD tool Papyrus for Realtime as a testbed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".