Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Pengzhou, Liang, Hongyuan, Chen, Tao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2025)
by: Chen, Pengzhou, et al.
Published: (2025)
Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
by: Ye, Yulong, et al.
Published: (2026)
by: Ye, Yulong, et al.
Published: (2026)
PromiseTune: Unveiling Causally Promising and Explainable Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2025)
by: Chen, Pengzhou, et al.
Published: (2025)
MMO: Meta Multi-Objectivization for Software Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2021)
by: Chen, Pengzhou, et al.
Published: (2021)
CoTune: Co-evolutionary Configuration Tuning
by: Xiong, Gangda, et al.
Published: (2025)
by: Xiong, Gangda, et al.
Published: (2025)
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
by: Chen, Pengzhou, et al.
Published: (2026)
by: Chen, Pengzhou, et al.
Published: (2026)
Dividable Configuration Performance Learning
by: Gong, Jingzhi, et al.
Published: (2024)
by: Gong, Jingzhi, et al.
Published: (2024)
Adapting Multi-objectivized Software Configuration Tuning
by: Chen, Tao, et al.
Published: (2024)
by: Chen, Tao, et al.
Published: (2024)
The Same Only Different: On Information Modality for Configuration Performance Analysis
by: Liang, Hongyuan, et al.
Published: (2025)
by: Liang, Hongyuan, et al.
Published: (2025)
Distilled Lifelong Self-Adaptation for Configurable Systems
by: Ye, Yulong, et al.
Published: (2025)
by: Ye, Yulong, et al.
Published: (2025)
Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
by: Xiang, Zezhen, et al.
Published: (2025)
by: Xiang, Zezhen, et al.
Published: (2025)
Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization
by: Ma, Youpeng, et al.
Published: (2025)
by: Ma, Youpeng, et al.
Published: (2025)
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
by: Gong, Jingzhi, et al.
Published: (2024)
by: Gong, Jingzhi, et al.
Published: (2024)
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
by: Akli, Amal, et al.
Published: (2026)
by: Akli, Amal, et al.
Published: (2026)
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents
by: Wang, Zehao, et al.
Published: (2024)
by: Wang, Zehao, et al.
Published: (2024)
On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
by: Chen, Xinyu, et al.
Published: (2024)
by: Chen, Xinyu, et al.
Published: (2024)
Exploringand Unleashing the Power of Large Language Models in CI/CD Configuration Translation
by: Wang, Chong, et al.
Published: (2025)
by: Wang, Chong, et al.
Published: (2025)
Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study
by: Hou, Xinyi, et al.
Published: (2025)
by: Hou, Xinyi, et al.
Published: (2025)
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning
by: Gong, Jingzhi, et al.
Published: (2024)
by: Gong, Jingzhi, et al.
Published: (2024)
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning
by: Wang, Xinyi, et al.
Published: (2025)
by: Wang, Xinyi, et al.
Published: (2025)
RisConFix: LLM-based Automated Repair of Risk-Prone Drone Configurations
by: Han, Liping, et al.
Published: (2025)
by: Han, Liping, et al.
Published: (2025)
Configuration Validation with Large Language Models
by: Lian, Xinyu, et al.
Published: (2023)
by: Lian, Xinyu, et al.
Published: (2023)
Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
by: Gong, Jingzhi, et al.
Published: (2025)
by: Gong, Jingzhi, et al.
Published: (2025)
Fine-Tuning Code Language Models to Detect Cross-Language Bugs
by: Li, Zengyang, et al.
Published: (2025)
by: Li, Zengyang, et al.
Published: (2025)
LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems
by: Wu, Siyu, et al.
Published: (2026)
by: Wu, Siyu, et al.
Published: (2026)
Unveiling Project-Specific Bias in Neural Code Models
by: Li, Zhiming, et al.
Published: (2022)
by: Li, Zhiming, et al.
Published: (2022)
Enhancing High-Quality Code Generation in Large Language Models with Comparative Prefix-Tuning
by: Jiang, Yuan, et al.
Published: (2025)
by: Jiang, Yuan, et al.
Published: (2025)
A Contemporary Survey of Large Language Model Assisted Program Analysis
by: Wang, Jiayimei, et al.
Published: (2025)
by: Wang, Jiayimei, et al.
Published: (2025)
Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
by: Sartaj, Hassan, et al.
Published: (2025)
by: Sartaj, Hassan, et al.
Published: (2025)
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
by: Ma, Wei, et al.
Published: (2022)
by: Ma, Wei, et al.
Published: (2022)
AI for DevSecOps: A Landscape and Future Opportunities
by: Fu, Michael, et al.
Published: (2024)
by: Fu, Michael, et al.
Published: (2024)
Causally Perturbed Fairness Testing
by: Du, Chengwen, et al.
Published: (2025)
by: Du, Chengwen, et al.
Published: (2025)
Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement
by: Wang, Shihai, et al.
Published: (2025)
by: Wang, Shihai, et al.
Published: (2025)
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
by: Chen, Jie, et al.
Published: (2024)
by: Chen, Jie, et al.
Published: (2024)
A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model
by: Zhou, Jingwen, et al.
Published: (2024)
by: Zhou, Jingwen, et al.
Published: (2024)
PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents
by: Yu, Bihui, et al.
Published: (2026)
by: Yu, Bihui, et al.
Published: (2026)
Pushing the Boundary: Specialising Deep Configuration Performance Learning
by: Gong, Jingzhi
Published: (2024)
by: Gong, Jingzhi
Published: (2024)
Instruction-Tuning Open-Weight Language Models for BPMN Model Generation
by: Çelikmasat, Gökberk, et al.
Published: (2025)
by: Çelikmasat, Gökberk, et al.
Published: (2025)
Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
by: Bethel, E. Wes, et al.
Published: (2024)
by: Bethel, E. Wes, et al.
Published: (2024)
How Many Tries Does It Take? Iterative Self-Repair in LLM Code Generation Across Model Scales and Benchmarks
by: Arimbur, Johin Johny
Published: (2026)
by: Arimbur, Johin Johny
Published: (2026)
Similar Items
-
Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2025) -
Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
by: Ye, Yulong, et al.
Published: (2026) -
PromiseTune: Unveiling Causally Promising and Explainable Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2025) -
MMO: Meta Multi-Objectivization for Software Configuration Tuning
by: Chen, Pengzhou, et al.
Published: (2021) -
CoTune: Co-evolutionary Configuration Tuning
by: Xiong, Gangda, et al.
Published: (2025)