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
IoT systems are rapidly adopted in various domains, from embedded systems to smart homes. Despite their growing adoption and popularity, there has been no thorough study to understand IoT development challenges from the practitioners' point of view. We provide the first systematic study of bugs and challenges that IoT developers face in practice, through a large-scale empirical investigation. We collected 5,565 bug reports from 91 representative IoT project repositories and categorized a random sample of 323 based on the observed failures, root causes, and the locations of the faulty components. In addition, we conducted nine interviews with IoT experts to uncover more details about IoT bugs and to gain insight into IoT developers' challenges. Lastly, we surveyed 194 IoT developers to validate our findings and gain further insights. We propose the first bug taxonomy for IoT systems based on our results. We highlight frequent bug categories and their root causes, correlations between them, and common pitfalls and challenges that IoT developers face. We recommend future directions for IoT areas that require research and development attention.
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