A comparison of code quality metrics and best practices in non-IoT and IoT systems
Bibliographic record
Abstract
IoT systems are a network of connected devices powered by software, requiring the study of software quality for maintenance. Despite extensive studies on non-IoT systems’ software quality, research on IoT systems’ software quality is lacking. It is uncertain whether non-IoT and IoT systems’ software are comparable, limiting the application of results and best practices from non-IoT to IoT systems. Therefore, we compare the code quality of two equivalent sets of non-IoT and IoT systems to determine whether there are similarities and differences between the two kinds of software systems. We design and apply a systematic method to select two sets of 94 non-IoT and IoT system software from GitHub with comparable characteristics. We compute quality metrics on the systems in these two sets and then analyse and compare the metric values. We conduct an in-depth analysis and provide specific examples of the IoT systems’ complexity and how it manifests in their source code. We conclude that software for IoT systems is more complex, coupled, larger, less maintainable, and cohesive than non-IoT systems. Several factors, such as integrating multiple hardware and software components and managing data communication between them, contribute to these differences. After the comparison, we systematically select and present a list of best practices to address the observed differences between non-IoT and IoT code. We present a list of revisited best practices with approaches, tools, or techniques for developing IoT systems. For example, applying modularity and refactoring are best practices for lowering complexity. Based on our work, researchers can now make informed decisions using existing studies on the quality of non-IoT systems for IoT systems. Developers can use the list of best practices to minimise disparities in complexity, size, and cohesion and enhance maintainability and code readability.
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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 | 0.015 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".