Copertina

Pushing the Boundary: Specialising Deep Configuration Performance Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Gong, Jingzhi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
di: Gong, Jingzhi, et al.
Pubblicazione: (2024)

Dividable Configuration Performance Learning
di: Gong, Jingzhi, et al.
Pubblicazione: (2024)

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
di: Xiang, Zezhen, et al.
Pubblicazione: (2025)

Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning
di: Gong, Jingzhi, et al.
Pubblicazione: (2024)

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
di: Chen, Pengzhou, et al.
Pubblicazione: (2025)

Predicting Software Performance with Divide-and-Learn
di: Gong, Jingzhi, et al.
Pubblicazione: (2023)

Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
di: Gong, Jingzhi, et al.
Pubblicazione: (2026)

GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization
di: Gong, Jingzhi, et al.
Pubblicazione: (2025)

Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization
di: Ma, Youpeng, et al.
Pubblicazione: (2025)

Prompting for Performance: Exploring LLMs for Configuring Software
di: Spieker, Helge, et al.
Pubblicazione: (2025)

Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration
di: Hymel, Cory, et al.
Pubblicazione: (2024)

Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents
di: Wang, Zehao, et al.
Pubblicazione: (2024)

Interpreting Performance Profiles with Deep Learning
di: Liu, Zhuoran
Pubblicazione: (2025)

Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach
di: Ashiga, Mari, et al.
Pubblicazione: (2025)

SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
di: Gong, Jingzhi, et al.
Pubblicazione: (2026)

Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
di: Gong, Jingzhi, et al.
Pubblicazione: (2025)

CoTune: Co-evolutionary Configuration Tuning
di: Xiong, Gangda, et al.
Pubblicazione: (2025)

Distilled Lifelong Self-Adaptation for Configurable Systems
di: Ye, Yulong, et al.
Pubblicazione: (2025)

Learning Software Bug Reports: A Systematic Literature Review
di: Long, Guoming, et al.
Pubblicazione: (2025)

RAT: RunAnyThing via Fully Automated Environment Configuration
di: Huang, Renhong, et al.
Pubblicazione: (2026)

Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design
di: Gong, Jingzhi
Pubblicazione: (2025)

irace-evo: Automatic Algorithm Configuration Extended With LLM-Based Code Evolution
di: Sartori, Camilo Chacón, et al.
Pubblicazione: (2025)

Exploringand Unleashing the Power of Large Language Models in CI/CD Configuration Translation
di: Wang, Chong, et al.
Pubblicazione: (2025)

PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
di: Hubert, Nicolas, et al.
Pubblicazione: (2023)

Empirical evaluation of LLMs in predicting fixes of Configuration bugs in Smart Home System
di: Monisha, Sheikh Moonwara Anjum, et al.
Pubblicazione: (2025)

On Security Weaknesses and Vulnerabilities in Deep Learning Systems
di: Lai, Zhongzheng, et al.
Pubblicazione: (2024)

IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
di: Nekrasov, Roman, et al.
Pubblicazione: (2025)

Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
di: Ye, Yulong, et al.
Pubblicazione: (2026)

RisConFix: LLM-based Automated Repair of Risk-Prone Drone Configurations
di: Han, Liping, et al.
Pubblicazione: (2025)

Deep Learning Library Testing: Definition, Methods and Challenges
di: Zhang, Xiaoyu, et al.
Pubblicazione: (2024)

Unraveling Code Clone Dynamics in Deep Learning Frameworks
di: Assi, Maram, et al.
Pubblicazione: (2024)

Tool-integrated Reinforcement Learning for Repo Deep Search
di: Ma, Zexiong, et al.
Pubblicazione: (2025)

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
di: Wan, Yao, et al.
Pubblicazione: (2023)

Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
di: Zhang, Zejun, et al.
Pubblicazione: (2026)

Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
di: Chen, Pengzhou, et al.
Pubblicazione: (2025)

SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
di: Xu, Duling, et al.
Pubblicazione: (2026)

Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
di: del Rey, Santiago, et al.
Pubblicazione: (2025)

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
di: Palacio, David N.
Pubblicazione: (2025)

Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?
di: Wang, Taiming, et al.
Pubblicazione: (2025)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
di: Aghili, Roozbeh, et al.
Pubblicazione: (2025)