Wellington Oliveira

Wellington Oliveira

Mobile devices have become ubiquitous tools for communication, entertainment, and productivity, yet battery autonomy remains a constraint. While energy-saving tips exist, they are often generic, anecdotal, or focused on software development rather than end-user behavior, leaving users to rely on grey literature or tacit knowledge to optimize their device energy consumption, lacking the academic rigor to ensure their effectiveness. This research aims to bridge the gap between technical energy analysis and practical user application by quantifying the energy consumption of different user-controlled parameters. Employing an automated monitoring framework, a series of user interface tests that simulate realistic usage patterns across popular applications (i.e., WhatsApp, Instagram, TikTok, and YouTube) was conducted. The objective is to have a systematic evaluation of the energy impact of user-controllable factors, including device settings, such as screen brightness, refresh rate, connectivity status, interface themes, and battery-saving profiles, combined with more app-specific variables (e.g., video resolution and message size). By analyzing over 12,000 data points, this paper quantifies the real-world impact of common settings, revealing the trade-offs between user experience and device autonomy.

Wellington Oliveira, Hugo Matalonga, Gustavo Pinto et al.

In the last few years, a growing body of research has proposed methods, techniques, and tools to support developers in the construction of software that consumes less energy. These solutions leverage diverse approaches such as version history mining, analytical models, identifying energy-efficient color schemes, and optimizing the packaging of HTTP requests. In this chapter, we present a complementary approach. We advocate that developers should leverage software diversity to make software systems more energy-efficient. Our main insight is that non-specialists can build software that consumes less energy by alternating at development time between readily available, diversely-designed pieces of software implemented by third-parties. These pieces of software can vary in nature, granularity, and quality attributes. Examples include data structures and constructs for thread management and synchronization.