Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study
Abstract
Advances in technologies like artificial intelligence and metaverse have led to a proliferation of software systems in business and everyday life. With this widespread penetration, the carbon emissions of software are rapidly growing as well, thereby negatively impacting the long-term sustainability of our environment. Hence, optimizing software from a sustainability standpoint becomes more crucial than ever. We believe that the adoption of automated tools that can identify energy-inefficient patterns in the code and guide appropriate refactoring can significantly assist in this optimization. In this extended abstract, we present an industry case study that evaluates the sustainability impact of refactoring energy-inefficient code patterns identified by automated software sustainability assessment tools for a large application. 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.
Cite
@article{arxiv.2506.09370,
title = {Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study},
author = {Rohit Mehra and Priyavanshi Pathania and Vibhu Saujanya Sharma and Vikrant Kaulgud and Sanjay Podder and Adam P. Burden},
journal= {arXiv preprint arXiv:2506.09370},
year = {2025}
}
Comments
3 pages. To be published in the proceedings of 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), Kirchberg, Luxembourg