Developer Attitudes and Practices Towards Optimizing Software Energy Consumption
- URL: http://arxiv.org/abs/2608.30527v1
- Date: Mon, 31 Aug 2026 09:57:36 GMT
- Title: Developer Attitudes and Practices Towards Optimizing Software Energy Consumption
- Abstract summary: Energy consumption is explicitly considered in only a minority of projects.<n>More commonly, developers influence energy use indirectly by optimizing proxy properties such as execution time and CPU utilization.<n>We identify organizational disincentives, limited tooling, and educational gaps as major barriers to adoption.
- Score: 9.378911615939925
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Integrating Energy Efficiency into Software Development: Developer Perspectives and Requirements [0.0]
This study investigates how developers perceive energy efficiency in their daily work and which requirements they formulate for AI-assisted tools supporting energy-aware development.<n>The results indicate that energy efficiency rarely plays an explicit role in daily development activities.<n>Identified barriers to the explicit consideration of energy efficiency include limited awareness and a strong focus on timely delivery.
arXiv Detail & Related papers (2026-07-24T10:14:31Z) - EnCoDe: Energy Estimation of Source Code At Design-Time [0.0]
Energy efficiency has emerged as a vital attribute of software quality, with significant implications for both environmental sustainability and operational costs.<n>Existing profiling tools operate only at runtime and coarse granularity, typically capturing energy at the process or method level.<n>We propose EnCoDe, a methodology for fine-grained, design-time energy estimation.
arXiv Detail & Related papers (2026-05-01T08:30:36Z) - Towards Green AI: Decoding the Energy of LLM Inference in Software Development [46.879983975894135]
AI-assisted tools are increasingly integrated into software development, but their reliance on large language models (LLMs) introduces substantial computational and energy costs.<n>We conduct a phase-level analysis of LLM inference energy consumption, distinguishing between the (1) prefill, where the model processes the input and builds internal representations, and (2) decoding, where output tokens are generated using the stored state.
arXiv Detail & Related papers (2026-02-05T14:38:19Z) - How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study [0.9099663022952497]
We examined the energy awareness of agent-authored pull requests (PRs) using a publicly available dataset.<n>We identified 216 energy-explicit PRs and conducted a thematic analysis, deriving a taxonomy of energy-aware work.<n>Although building and running these agents is highly energy intensive, encouragingly, the results indicate that they exhibit energy awareness when generating software artifacts.
arXiv Detail & Related papers (2025-12-31T05:13:56Z) - Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study [9.521952718902973]
We present an industry case study that evaluates the sustainability impact of energy-inefficient code patterns identified by automated software sustainability assessment tools.<n>Preliminary results highlight a positive impact on the application's sustainability post-refactoring, leading to a 29% decrease in per-user per-month energy consumption.
arXiv Detail & Related papers (2025-06-11T03:34:45Z) - ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [56.961539386979354]
We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
arXiv Detail & Related papers (2025-06-02T05:11:21Z) - Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations [45.243401722182554]
Large language models (LLMs) claim to assist developers in optimizing code for performance and energy efficiency.<n>This work focuses on software written in Matlab-widely used in both academia and industry for scientific and engineering applications.<n>We analyze energy-focused optimization on 400 scripts across 100 top GitHub repositories.
arXiv Detail & Related papers (2025-03-26T00:27:29Z) - How do Practitioners Perceive Energy Consumption on Stack Overflow? [3.000496428347787]
We conduct an empirical analysis of Stack Overflow (SO) questions concerning energy consumption.
These questions reflect real-world energy-related predicaments faced by practitioners in their daily development activities.
Our observations raise awareness among practitioners about the impact of energy consumption on developing software systems.
arXiv Detail & Related papers (2024-09-28T03:28:52Z) - Impact of ML Optimization Tactics on Greener Pre-Trained ML Models [46.78148962732881]
This study aims to (i) analyze image classification datasets and pre-trained models, (ii) improve inference efficiency by comparing optimized and non-optimized models, and (iii) assess the economic impact of the optimizations.
We conduct a controlled experiment to evaluate the impact of various PyTorch optimization techniques (dynamic quantization, torch.compile, local pruning, and global pruning) to 42 Hugging Face models for image classification.
Dynamic quantization demonstrates significant reductions in inference time and energy consumption, making it highly suitable for large-scale systems.
arXiv Detail & Related papers (2024-09-19T16:23:03Z) - Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
We study relevant models in the areas of computer vision and natural language processing.
For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
arXiv Detail & Related papers (2021-09-12T09:40:18Z) - Towards the Systematic Reporting of the Energy and Carbon Footprints of
Machine Learning [68.37641996188133]
We introduce a framework for tracking realtime energy consumption and carbon emissions.
We create a leaderboard for energy efficient reinforcement learning algorithms.
We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
arXiv Detail & Related papers (2020-01-31T05:12:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.