A systematic literature review on Android-specific smells

Wu, Zhiqiang; Chen, Xin; Lee, Scott Uk-Jin

doi:10.1016/j.jss.2023.111677

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…In this study they also analyze 3 "code smells" typical of object-oriented programming, finding that 93% of Java and Kotlin applications contain at least one [15]. In 2023, Z. Wu et al carried out a literature review on Android-specific "code smells" cataloging the anti-patterns that receive the most attention in literature [41]; while F. Palomba et al show that not all "smells" have an equal impact on energy efficiency [31]. M. Couto et al, on the other hand, studied the impact of refactoring by considering a large number of different applications [8].…”

Section: Related Workmentioning

confidence: 99%

Real-time Energy-efficient Sensor Libraries for Wearable Devices

Calisti,

Lattanzi

2023

Preprint

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The growing popularity of wearable technology has led to a surge in smartwatch usage among the general public. These devices offer a range of features, including internet connectivity, fitness tracking, and real-time notifications, making them valuable tools for staying connected to the online world while remaining engaged in real-world activities. Smartwatches have become powerful platforms for Human Activity Recognition (HAR) applications thanks to the increasing computational power and the presence of a wide array of sensors, such as accelerometers, gyroscopes, heart rate, and step counters. Efficient real-time data collection from internal sensors is a crucial requirement for HAR applications on wearable devices due to their constraints in battery size and duration. In this paper, we introduce the implementation of three energy-efficient user-level libraries developed for real-time data collection from inertial sensors using native Wear OS APIs and different techniques: Thread, Flow, and Channel. Experiments were conducted on a commercially available Oppo smartwatch comparing them in terms of code size, memory utilization, and energy consumption. The characterization results demonstrate empirically that the solution is lightweight and adaptable, making it suitable for development on wearable devices without significant impact on battery life and system performance.

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