Practical Guidance for IoT Systems Testing: A Taxonomy
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.
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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 | 0.037 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".