Addressing non-functional requirements of adaptive IoT systems
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
Non-functional requirements (NFR) of IoT systems increase the complexity of system development. The success of such systems also largely depends on dealing with NFRs correctly. However, inter-dependencies among NFRs often introduce conflicts. These conflicts impede implementing the system with all specified NFRs. Furthermore, the heterogeneous nature of IoT systems makes it critical to incorporate NFRs in the early stages of software development. This PhD thesis proposes a model-driven requirements engineering procedure to address different NFRs of adaptive IoT systems. This approach will incorporate non-functional requirements at different levels of abstraction with model-driven techniques to minimize conflicts among elicited NFRs. We are extending use case models, soft goal models, and behavioural models to elicit, analyze, and specify interoperability, scalability, availability, and context-awareness of IoT systems. As interoperability and context-awareness are two NFRs that affect the adaptiveness of IoT systems most, we addressed these two NFRs first. Availability and scalability NFRs will be incorporated as this thesis progresses.
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 | 0.000 | 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 | 0.000 | 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