An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables

Abstract

The growth of the Internet of Things (IoT), particularly in wearable devices like Fitbits, has raised challenges related to source code quality, testing, data accuracy, and practical applications. This paper investigates issues in Fitbit apps by (1) analyzing GitHub repositories of Fitbit projects to identify code quality issues, (2) using Large Language Models (LLMs) to automate testing, (3) comparing data variations across different Fitbit models, and (4) experimenting with real-world use cases for Fitbit devices. Our analysis of $\mathbf{1 6}$ GitHub repositories revealed code quality issues in Fitbit apps, highlighting the need for better practices. Using LLMs like ChatGPT-4, we generated unit tests with $100 \%$ coverage. Data comparisons across Fitbit Versa models showed consistent accuracy. Finally, we showed the potential of wearable devices in the real-world with two practical use cases: health monitoring with robotic assistance and location-based tracking. These findings open new avenues for research in wearables.