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

2023-09-11

International Conference on Automated Software Engineering

AI Summary

The rapid proliferation of AI and metaverse applications has led to a sharp increase in software carbon emissions, posing a critical threat to environmental sustainability. Method: This paper proposes an energy-efficiency–oriented, code-level refactoring approach. It introduces the first systematic, industrial-scale empirical quantification of how code-pattern refactoring impacts software energy consumption, establishing an evidence-based optimization framework integrating static analysis, fine-grained energy modeling, and an automated refactoring toolchain. The method automatically identifies and transforms energy-inefficient code patterns, delivering actionable, production-ready refactoring guidance. Contribution/Results: Experiments on large-scale industrial applications demonstrate a 29% reduction in per-user monthly energy consumption post-refactoring, significantly enhancing operational energy efficiency and carbon sustainability. This work bridges a critical gap in green software engineering by providing the first industry-validated, reproducible, and generalizable methodology for code-level sustainability optimization.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Metareasoning and Metaheuristics
• Application Domains: Software Engineering

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Impact of refactoring energy-inefficient code on sustainability
• Reducing carbon emissions via automated code optimization
• Evaluating energy savings from refactoring in large applications

Innovation

• Automated tools identify energy-inefficient code
• Refactoring reduces software energy consumption
• Case study shows 29% energy decrease