Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
Yulong Ye, Hongyuan Liang, Chao Jiang, Miqing Li, and Tao Chen

TL;DR
This paper introduces Domland, a methodology combining fitness landscape analysis and domain knowledge to explain configuration tuning effectiveness and challenges across diverse software systems.
Contribution
It presents a novel approach that integrates spatial fitness landscape analysis with domain insights to interpret and improve configuration tuning processes.
Findings
Configuration landscapes are system-specific.
Core options significantly influence landscape ruggedness.
Workload effects on landscapes vary by system.
Abstract
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 configurable systems. Prior efforts predominantly adopt static domain analysis (e.g., static taint analysis), which often lacks generalizability, or dynamic data analysis (e.g., benchmarking performance analysis), limiting explainability. In this work, we embrace Fitness Landscape Analysis (FLA) as a bridge between domain knowledge and difficulty of the tuning. We propose Domland, a two-pronged methodology that synergizes the spatial information obtained from FLA and domain-driven analysis to systematically capture the hidden characteristics of configuration tuning cases, explaining how and why a tuner might succeed or fail. This helps to better interpret and contextualize the behavior of tuners and inform tuner design.…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Testing and Debugging Techniques · Advanced Software Engineering Methodologies
