"ENERGY STAR" LLM-Enabled Software Engineering Tools

TL;DR

This paper investigates the energy efficiency of AI-enabled software engineering tools, focusing on Large Language Models, and proposes a framework to measure and improve their energy consumption during code generation.

Contribution

It introduces a comprehensive framework combining RAG and PETs to enhance energy efficiency and quality of LLM-based code generation in software engineering tools.

Findings

Measured real-time energy consumption across various LLM architectures.

Demonstrated the effectiveness of RAG and PETs in reducing energy use.

Provided a proof of concept for energy-efficient LLM-based SE tools.

Abstract

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.