Addressing non-functional requirements of adaptive IoT systems
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.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".