Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study

TL;DR

This paper presents an industry case study demonstrating that refactoring energy-inefficient code patterns with automated tools can significantly reduce software energy consumption, thereby enhancing sustainability.

Contribution

It introduces a practical approach to improve software sustainability through automated identification and refactoring of energy-inefficient code patterns, validated by real-world case study results.

Findings

29% reduction in per-user per-month energy consumption

Positive impact on software sustainability after refactoring

Supports automated tools for energy-efficient software development

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.