# Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis

arXiv: https://arxiv.org/abs/1709.02280 · HTML: https://mlsanity.com/paper/1709.02280/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Unicorn: Reasoning about Configurable System Performance through the lens of Causality](https://mlsanity.com/paper/2201.08413/) (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)
- [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)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [An Exploratory Study of Log Placement Recommendation in an Enterprise System](https://mlsanity.com/paper/2103.01755/) (2021)
- [CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots](https://mlsanity.com/paper/2301.07690/) (2023)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective](https://mlsanity.com/paper/2412.16888/) (2024)
- [CURE: Simulation-Augmented Auto-Tuning in Robotics](https://mlsanity.com/paper/2402.05399/) (2024)
- [Linux Kernel Configurations at Scale: A Dataset for Performance and Evolution Analysis](https://mlsanity.com/paper/2505.07487/) (2025)
- [Prompting for Performance: Exploring LLMs for Configuring Software](https://mlsanity.com/paper/2507.09790/) (2025)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)
- [When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets](https://mlsanity.com/paper/2607.16476/) (2026)
- [Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks](https://mlsanity.com/paper/2212.13359/) (2022)
- [Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling](https://mlsanity.com/paper/2509.11000/) (2025)
- [SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification](https://mlsanity.com/paper/2607.00113/) (2026)

## Abstract (arXiv)

Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configuration space. Recently, transfer learning has been applied to reduce the effort of constructing performance models by transferring knowledge about performance behavior across environments. While this line of research is promising to learn more accurate models at a lower cost, it is unclear why and when transfer learning works for performance modeling. To shed light on when it is beneficial to apply transfer learning, we conducted an empirical study on four popular software systems, varying software configurations and environmental conditions, such as hardware, workload, and software versions, to identify the key knowledge pieces that can be exploited for transfer learning. Our results show that in small environmental changes (e.g., homogeneous workload change), by applying a linear transformation to the performance model, we can understand the performance behavior of the target environment, while for severe environmental changes (e.g., drastic workload change) we can transfer only knowledge that makes sampling more efficient, e.g., by reducing the dimensionality of the configuration space.