most citedRevealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2026

When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets

Chao Jiang, Yulong Ye, Tao Chen +1

Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade. Yet, the time required during the tuning proce…

cs.LG2026

LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems

Siyu Wu, Yulong Ye, Zezhen Xiang +3

Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperparameter optimization (HPO) for t…

cs.SE20261 cited

Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes

Yulong Ye, Hongyuan Liang, Chao Jiang +2

Configuration tuning for better performance is crucial in quality assurance. Yet, there has long been a mystery on tuners' effectiveness, due to the black-box nature of configurabl…

cs.SE2026

MOOT: a Repository of Many Multi-Objective Optimization Tasks

Tim Menzies, Tao Chen, Yulong Ye +4

Software engineers must make decisions that trade off competing goals (faster vs. cheaper, secure vs. usable, accurate vs. interpretable, etc.). Despite MSR's proven techniques for…

cs.SE2025

Distilled Lifelong Self-Adaptation for Configurable Systems

Yulong Ye, Tao Chen, Miqing Li

Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the conf…