Copertina

Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Apsan, Radu, Stoico, Vincenzo, Albonico, Michel, Dhar, Rudra, Vaidhyanathan, Karthik, Malavolta, Ivano
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LLM-based Automated Architecture View Generation: Where Are We Now?
di: Sathvika, Miryala, et al.
Pubblicazione: (2026)

Ten Years of Teaching Empirical Software Engineering in the context of Energy-efficient Software
di: Malavolta, Ivano, et al.
Pubblicazione: (2024)

AgenticAKM : Enroute to Agentic Architecture Knowledge Management
di: Dhar, Rudra, et al.
Pubblicazione: (2026)

Context Matters: Evaluating Context Strategies for Automated ADR Generation Using LLMs
di: Gupta, Aviral, et al.
Pubblicazione: (2026)

A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama
di: Cursaru, Vlad-Andrei, et al.
Pubblicazione: (2024)

Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study
di: Dhar, Rudra, et al.
Pubblicazione: (2024)

What Is the Cost of Energy Monitoring? An Empirical Study on the Overhead of RAPL-Based Tools
di: Diamond, Jeremy, et al.
Pubblicazione: (2026)

An Empirical Study on How Architectural Topology Affects Microservice Performance and Energy Usage
di: Ristova, Irena, et al.
Pubblicazione: (2026)

Can Large Language Models Assist the Comprehension of ROS2 Software Architectures?
di: Duits, Laura, et al.
Pubblicazione: (2026)

EnCoDe: Energy Estimation of Source Code At Design-Time
di: Goyal, Shailender, et al.
Pubblicazione: (2026)

DRAFT-ing Architectural Design Decisions using LLMs
di: Dhar, Rudra, et al.
Pubblicazione: (2025)

A Curated List of Open-source Software-only Energy Efficiency Measurement Tools: A GitHub Mining Study
di: Cannizza, Manuela Bechara, et al.
Pubblicazione: (2026)

An Empirical Study on the Performance and Energy Usage of Compiled Python Code
di: Stoico, Vincenzo, et al.
Pubblicazione: (2025)

Reimagining Self-Adaptation in the Age of Large Language Models
di: Donakanti, Raghav, et al.
Pubblicazione: (2024)

CALM: A Self-Adaptive Orchestration Approach for QoS-Aware Routing in Small Language Model based Systems
di: Jain, Hemang, et al.
Pubblicazione: (2026)

Performance Antipatterns: Angel or Devil for Power Consumption?
di: Aneggi, Alessandro, et al.
Pubblicazione: (2026)

LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World
di: Arun, Shrikara, et al.
Pubblicazione: (2025)

Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models
di: Cortellessa, Vittorio, et al.
Pubblicazione: (2023)

Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry?
di: Kuran, Pelin Rabia, et al.
Pubblicazione: (2026)

Toward an Architectural Blueprint to Observe Sustainability in and by Software Systems
di: Toczé, Klervie, et al.
Pubblicazione: (2026)

Can AI Agents Generate Microservices? How Far are We?
di: Adnan, Bassam, et al.
Pubblicazione: (2026)

ArchBench: Benchmarking Generative-AI for Software Architecture Tasks
di: Adnan, Bassam, et al.
Pubblicazione: (2026)

EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems
di: Tedla, Meghana, et al.
Pubblicazione: (2024)

Architecting AgentOps Needs CHANGE
di: Biswas, Shaunak, et al.
Pubblicazione: (2026)

SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems
di: Marda, Arya, et al.
Pubblicazione: (2024)

Toward architecting self-coding information systems
di: Falcão, Rodrigo, et al.
Pubblicazione: (2026)

SWEnergy: An Empirical Study on Energy Efficiency in Agentic Issue Resolution Frameworks with SLMs
di: Tripathy, Arihant, et al.
Pubblicazione: (2025)

Where Is Self-admitted Code Generated by Large Language Models on GitHub?
di: Yu, Xiao, et al.
Pubblicazione: (2024)

Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Calling
di: Kavathekar, Ishan, et al.
Pubblicazione: (2025)

The Sustainability Assessment Framework Toolkit: A Decade of Modeling Experience
di: Lago, Patricia, et al.
Pubblicazione: (2024)

POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems?
di: Pandey, Divyansh, et al.
Pubblicazione: (2025)

Towards Architecting Sustainable MLOps: A Self-Adaptation Approach
di: Bhatt, Hiya, et al.
Pubblicazione: (2024)

Approach Towards Semi-Automated Certification for Low Criticality ML-Enabled Airborne Applications
di: Sridhar, Chandrasekar, et al.
Pubblicazione: (2025)

How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
di: Omar, Rafiullah, et al.
Pubblicazione: (2024)

Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
di: Ashraf, Humza, et al.
Pubblicazione: (2025)

Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
di: Peng, Huiyun, et al.
Pubblicazione: (2024)

Efficient Code Analysis via Graph-Guided Large Language Models
di: Gao, Hang, et al.
Pubblicazione: (2026)

Leveraging LLMs for Dynamic IoT Systems Generation through Mixed-Initiative Interaction
di: Adnan, Bassam, et al.
Pubblicazione: (2025)

APDT: A Digital Twin for Assessing Access Point Characteristics in a Network
di: Yashaswinee, D. Sree, et al.
Pubblicazione: (2025)

EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices
di: Matathammal, Akhila, et al.
Pubblicazione: (2025)