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

arXiv: https://arxiv.org/abs/2603.19897 · HTML: https://mlsanity.com/paper/2603.19897/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 100 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 30 of 34)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Finding Faster Configurations using FLASH](https://mlsanity.com/paper/1801.02175/) (2018)
- [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.03157/) (2023)
- [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021)
- [Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2110.12654/) (2021)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [Improving Protein Optimization with Smoothed Fitness Landscapes](https://mlsanity.com/paper/2307.00494/) (2023)
- [Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis](https://mlsanity.com/paper/1709.02280/) (2017)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems](https://mlsanity.com/paper/1609.03590/) (2016)
- [An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems](https://mlsanity.com/paper/1606.06543/) (2016)
- [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025)
- [PromiseTune: Unveiling Causally Promising and Explainable Configuration Tuning](https://mlsanity.com/paper/2507.05995/) (2025)
- [Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning](https://mlsanity.com/paper/2501.01876/) (2025)
- [White-Box Performance-Influence Models: A Profiling and Learning Approach](https://mlsanity.com/paper/2102.06395/) (2021)
- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- ["Sampling"' as a Baseline Optimizer for Search-based Software Engineering](https://mlsanity.com/paper/1608.07617/) (2016)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025)
- [An Evolutionary Study of Configuration Design and Implementation in Cloud Systems](https://mlsanity.com/paper/2102.07052/) (2021)
- [Towards offensive language detection and reduction in four Software Engineering communities](https://mlsanity.com/paper/2106.02245/) (2021)
- [The Same Only Different: On Information Modality for Configuration Performance Analysis](https://mlsanity.com/paper/2501.15475/) (2025)
- [Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem](https://mlsanity.com/paper/2205.13231/) (2022)

## Built on by (most cited first, 5 of 5)

- [Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation](https://mlsanity.com/paper/2604.21380/) (2026)
- [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026)
- [Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning](https://mlsanity.com/paper/2604.18862/) (2026)
- [LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems](https://mlsanity.com/paper/2605.08305/) (2026)
- [Configuring RAG Systems: What Breaks, Where Originates, and How to Fix](https://mlsanity.com/paper/2610.18567/) (2026)

## Abstract (arXiv)

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. To evaluate Domland, we conduct a case study of nine software systems and 93 workloads, from which we reveal several key findings: (1) configuration landscapes are inherently system-specific, with no single domain factor (e.g., system area, programming language, or resource intensity) consistently shaping their structure; (2) the core options (e.g., pic-struct of x264), which control the main functional flows, exert a stronger influence on landscape ruggedness (i.e. the difficulty of tuning) compared to resource options (e.g., cpu-independent of x264); (3) Workload effects on landscape structure are not uniformly tied to type or scale. Both contribute to landscape variations, but their impact is system-dependent.