Energy Impact of Method-Level Code Smells in AI-based applications

• Grant number: 25/04367-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2025
• End date: June 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Markus Endler
• Grantee: Guilherme Cunha Ribeiro
• Host Institution: Centro Técnico Científico (CTC). Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
• Associated research grant: 23/00811-0 - EcoSustain: computer and data science for the environment, AP.TEM

Abstract

The rapid growth of Artificial Intelligence (AI) adoption has driven the demand for high-performance computing infrastructure, resulting in significant energy consumption. This scenario raises environmental concerns, particularly regarding the sustainability of AI models and the impact of their processing on data centers and computing devices. This project investigates how poor coding practices in AI-based systems influence energy consumption by identifying code patterns that lead to inefficient use of computational resources. The research fellow will develop a systematic approach to detect these patterns (code smells) and propose refactorings that enhance energy efficiency. The results will contribute to advancing sustainable practices in software engineering, encouraging the development of more eco-friendly and energy-efficient solutions for AI applications.