Energy-Efficient Software Development: A Multi-dimensional Empirical Analysis of Stack Overflow

TL;DR

This study analyzes 1,193 Stack Overflow questions to understand real-world developer concerns and perceptions about energy consumption in software development, revealing key challenges and areas for optimization.

Contribution

It provides a comprehensive empirical analysis of energy-related developer questions on Stack Overflow, highlighting prevalent topics, challenges, and perceptions across multiple system levels.

Findings

Balancing Positioning usage is the most discussed energy topic.

Handling data efficiently poses significant challenges with longer response times.

Developers ask mainly to understand concepts or APIs related to energy consumption.

Abstract

Energy consumption of software applications has emerged as a critical concern for developers to contemplate in their daily development processes. Previous studies have surveyed a limited number of developers to understand their viewpoints on energy consumption. We complement these studies by analyzing a meticulously curated dataset of 1,193 Stack Overflow (SO) questions concerning energy consumption. These questions capture real-world energy-related challenges in practice. To understand practitioners' perceptions, we investigate the intentions behind these questions, semantic topics, and associated technologies (e.g., programming languages). Our results reveal that: (i) the most prevalent energy consumption topic is about balancing Positioning usage; (ii) efficiently handling data is particularly challenging, with these questions having the longest response times; (iii) practitioners primarily ask questions to understand a concept or API related to energy consumption; and (iv) practitioners are concerned about energy consumption across multiple levels-hardware, operating systems, and programming languages-during energy efficient software development. Our findings raise awareness about energy consumption's impact on software development. We also derive actionable implications for energy optimization at different levels (e.g., optimizing API usage or hardware accesses) during energy-aware software development.