Practical Guidance for IoT Systems Testing: A Taxonomy
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 Internet of Things (IoT) has transformed the way we interact with technology and devices. Several IoT systems are being deployed across diverse domains. They fulfill critical tasks and, thus, must function correctly and securely and meet the users' expectations. However, testing IoT systems poses many challenges, primarily due to their distributed nature, dynamism, and heterogeneity as well as the multiple layers of which they are composed, i.e., device, edge, cloud, and application layers. The absence of testing guidance can hinder the quality of IoT systems. Testing guidelines, including taxonomy, are vital for proper IoT systems testing. In the context of software testing, taxonomy organizes and categorizes testing aspects, helping testers to understand what, how, and when to test. However, no IoT systems testing taxonomy exists, and traditional software testing taxonomy may not sufficiently meet IoT systems testing requirements. To address this, we introduce an IoT-specific testing taxonomy, informed by a review of 83 primary studies and validated through surveys with 16 IoT industry practitioners. The feedback collected from practitioners shows that our taxonomy can help IoT testers to improve efficiency of testing. This taxonomy can help the testers to increase test coverage, enhance the efficiency and effectiveness of testing efforts, and ensure thorough testing of important system aspects, thus ensuring functional correctness, improving the security of IoT systems, and better meeting users' expectations.
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 | 0.000 |
| 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