Green My LLM: Studying the key factors affecting the energy consumption of code assistants
- URL: http://arxiv.org/abs/2411.11892v1
- Date: Thu, 07 Nov 2024 16:00:29 GMT
- Title: Green My LLM: Studying the key factors affecting the energy consumption of code assistants
- Authors: Tristan Coignion, Clément Quinton, Romain Rouvoy,
- Abstract summary: This paper investigates the energy consumption of large language models-based code assistants such as GitHub Copilot.
Our findings reveal that the energy consumption and performance of code assistants are influenced by various factors, such as the number of concurrent developers.
- Score: 1.747820331822631
- License:
- Abstract: In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environments (IDEs) as code assistants. These assistants, such as GitHub Copilot, deliver real-time code suggestions and can greatly enhance developers' productivity. However, the environmental impact of these tools, in particular their energy consumption, remains a key concern. This paper investigates the energy consumption of LLM-based code assistants by simulating developer interactions with GitHub Copilot and analyzing various configuration factors. We collected a dataset of development traces from 20 developers and conducted extensive software project development simulations to measure energy usage under different scenarios. Our findings reveal that the energy consumption and performance of code assistants are influenced by various factors, such as the number of concurrent developers, model size, quantization methods, and the use of streaming. Notably, a substantial portion of generation requests made by GitHub Copilot is either canceled or rejected by developers, indicating a potential area for reducing wasted computations. Based on these findings, we share actionable insights into optimizing configurations for different use cases, demonstrating that careful adjustments can lead to significant energy savings.
Related papers
- Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software? [2.8812501020074968]
Energy-efficient software engineering techniques are often absent from undergraduate curricula.
We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class.
arXiv Detail & Related papers (2024-10-30T01:09:32Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects [0.0]
GitHub Copilot is an AI-powered coding assistant.
This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot.
arXiv Detail & Related papers (2024-06-25T19:51:21Z) - Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data [49.1574468325115]
ChatGPT is an AI tool that enhances software production efficiency.
We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people.
These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
arXiv Detail & Related papers (2024-06-16T19:11:15Z) - A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions [13.58143103712]
GitHub Copilot is a large language model (LLM)-powered code generation tool.
This paper investigates how developers validate and repair code generated by Copilot.
Being aware of the code's provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload.
arXiv Detail & Related papers (2024-05-25T06:20:01Z) - DevBench: A Comprehensive Benchmark for Software Development [72.24266814625685]
DevBench is a benchmark that evaluates large language models (LLMs) across various stages of the software development lifecycle.
Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench.
Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
arXiv Detail & Related papers (2024-03-13T15:13:44Z) - Learn to Code Sustainably: An Empirical Study on LLM-based Green Code
Generation [7.8273713434806345]
We evaluate the sustainability of auto-generate codes produced by generative commercial AI language models.
We compare the performance and green capacity of human-generated code and code generated by the three AI language models.
arXiv Detail & Related papers (2024-03-05T22:12:01Z) - MAgIC: Investigation of Large Language Model Powered Multi-Agent in
Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing.
As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework.
This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
arXiv Detail & Related papers (2023-11-14T21:46:27Z) - SoTaNa: The Open-Source Software Development Assistant [81.86136560157266]
SoTaNa is an open-source software development assistant.
It generates high-quality instruction-based data for the domain of software engineering.
It employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA.
arXiv Detail & Related papers (2023-08-25T14:56:21Z) - The GitHub Development Workflow Automation Ecosystems [47.818229204130596]
Large-scale software development has become a highly collaborative endeavour.
This chapter explores the ecosystems of development bots and GitHub Actions.
It provides an extensive survey of the state-of-the-art in this domain.
arXiv Detail & Related papers (2023-05-08T15:24:23Z) - Code Recommendation for Open Source Software Developers [32.181023933552694]
CODER is a novel graph-based code recommendation framework for open source software developers.
Our framework achieves superior performance under various experimental settings, including intra-project, cross-project, and cold-start recommendation.
arXiv Detail & Related papers (2022-10-15T16:40:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.