Advances in Requirements Engineering for IoT and CPS: A Survey on Model-Driven Research and Practices
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
The engineering of Internet of Things (IoT) and Cyber-Physical Systems (CPS) bring about various challenges due to the inherent complexities associated with such heterogeneous systems. This calls for robust approaches for capturing, modelling, analyzing, and managing system requirements in an effective manner. We present a survey of current research and practices on model-driven requirements engineering for IoT and CPS. We analyze the languages, methods, tools, and model transformations used to deal with the specific challenges arising in this domain. Despite significant progress made over the last decade, the existing literature remains fragmented. Most approaches concentrate on specific phases of requirements engineering, such as specification or validation, and very few address the entire lifecycle from elicitation to management and traceability. Our findings also reveal a significant focus on model transformations to automate requirements engineering tasks. The limited integration of non-functional requirements in the survey also emerges as a prominent finding.
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 | 0.001 | 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 | 0.000 | 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 it