Developer Attitudes and Practices Towards Optimizing Software Energy Consumption
Max Weber, Alina Mailach, Florian Sattler, Sven Apel, Norbert Siegmund
Abstract
Context: Software significantly influences the efficiency with which hardware resources are utilized, yet software energy consumption is seldom treated as a first-class concern in day-to-day development practice. Objective: This study investigates professional developers' attitudes, decision-making, and development practices related to software energy consumption, with particular emphasis on how energy considerations are recognized, assessed, and acted upon during software development. Method: To this end, we conduct an online survey with 134 software developers. Our study combines quantitative analyses with a qualitative open-card sorting of free-text responses to characterize perceptions, practices, and reasoning patterns around energy consumption. Findings: Energy consumption is explicitly considered in only a minority of projects. More commonly, developers influence energy use indirectly by optimizing proxy properties such as execution time and CPU utilization. Responses to scenario-based questions reveal systematic blind spots in this mental model, including cases in which performance improvements increase energy consumption or exhibit no correlation. We also identify organizational disincentives, limited tooling, and educational gaps as major barriers to adoption. Implications: (1) Institutionalize energy-aware approaches through visible flagship deployments that demonstrate value, (2) expand research and education on energy-performance trade-offs, and (3) develop practical, developer-oriented measurement and feedback tools that lower adoption barriers.
Create a lesson
Related papers
ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks
Jeonghye Kim, Minseon Kim, Young Jin Kim et al.
Evaluating the Health of Open-Source Smart City Platforms
Rodrigo Bravo Simões, Fernando Brito e Abreu, Vasco Amaral
From Component Snapshots to Lifecycle Traces: Agent-Based Software Composition Analysis
Chaofan Li, Zhengduo Xue, Chengxiang Li et al.
A Study on the Impact of Natural Language Differences in Prompts on Automatic Code Generation Using LLMs
Haruka Tokumasu, Masanari Kondo, Alexander Serebrenik et al.
A Study of the Reliability of Agentic AI-Generated Programs
Ayesha Shafique, Barton P. MIller, Elisa R. Heymann
Relationally Guided Use Case Modeling with LLMs
Guangyu Wang, Bangqi Li, Ji Wu et al.