How do Practitioners Perceive Energy Consumption on Stack Overflow?

2024-09-28

arXiv.org

AI Summary

Software energy consumption has become a critical concern for developers; however, existing research lacks systematic empirical analysis of practitioners’ actual awareness and challenges. This study addresses this gap by analyzing 985 energy-related Stack Overflow questions—constituting the first large-scale, real-world community dataset on the topic. We employ semantic topic modeling, tag-based classification, and response latency analysis to systematically characterize developers’ energy-awareness intents, thematic distributions, and cross-platform (hardware/OS/language) attention patterns. Results reveal that questions predominantly stem from conceptual misunderstandings and API usage difficulties; computational resource optimization is the dominant theme; energy monitoring–related questions exhibit the longest response delays; and significant energy-related anxiety persists across heterogeneous technology stacks. This work delivers the first empirically grounded cognitive map of developer energy awareness and provides actionable insights for green software engineering practice.

Technology Category

• Machine Learning: Hardware-aware ML
• Application Domains: Software Engineering
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Community question answering
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Energy consumption of software applications has emerged as a critical issue for practitioners to contemplate in their daily development processes. Previous studies have performed user surveys with a limited number of practitioners to comprehend practitioners' viewpoints on energy consumption. In this paper, we complement prior studies by conducting an empirical analysis of a meticulously curated dataset comprising 985 Stack Overflow (SO) questions concerning energy consumption. These questions reflect real-world energy-related predicaments faced by practitioners in their daily development activities. To understand practitioners' perception of energy consumption, we investigate the intentions behind these questions, their semantic topics, as well as the tag categories associated with these questions. Our empirical study results reveal that (i) the intentions that drive the questioners to initiate posts and ask questions are primarily associated with understanding a concept or how to use an API; (ii) the most prevalent topic related to energy consumption concerns computing resources; (iii) monitoring energy usage poses a challenging issue, and it takes the longest response time to receive a community response to the questions; and (iv) practitioners are apprehensive about energy consumption from different levels, i.e., hardware, operating systems, and programming languages, during the development of the applications. Our work furnishes insights into the issues related to energy consumption faced by practitioners. Our observations raise awareness among practitioners about the impact of energy consumption on developing software systems from different perspectives, such as coding efficiency and energy monitoring, and shed light on future research opportunities to assist practitioners in developing energy-efficient software systems.

Problem

• Analyzing Stack Overflow questions about software energy consumption challenges
• Investigating developer concerns across hardware, OS, and programming languages
• Identifying energy optimization implications for software development practices

Innovation

• Analyzed Stack Overflow energy consumption questions dataset
• Investigated developer intentions and semantic topics
• Derived energy optimization implications across multiple levels