# Linux Kernel Configurations at Scale: A Dataset for Performance and Evolution Analysis

arXiv: https://arxiv.org/abs/2505.07487 · HTML: https://mlsanity.com/paper/2505.07487/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 8 of 8)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Easy over Hard: A Case Study on Deep Learning](https://mlsanity.com/paper/1703.00133/) (2017)
- [Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis](https://mlsanity.com/paper/1709.02280/) (2017)
- [Learning Software Configuration Spaces: A Systematic Literature Review](https://mlsanity.com/paper/1906.03018/) (2019)
- [Using Bad Learners to find Good Configurations](https://mlsanity.com/paper/1702.05701/) (2017)
- [ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel](https://mlsanity.com/paper/2012.15342/) (2020)
- [Transfer Learning with Bellwethers to find Good Configurations](https://mlsanity.com/paper/1803.03900/) (2018)
- [Maximizing Patch Coverage for Testing of Highly-Configurable Software without Exploding Build Times](https://mlsanity.com/paper/2404.17966/) (2024)

## Built on by (most cited first, 2 of 2)

- [LLM-Driven Kernel Evolution: Automating Driver Updates in Linux](https://mlsanity.com/paper/2511.18924/) (2025)
- [The FIL Hypothesis: Inductive Biases Help with Kernel Engineering](https://mlsanity.com/paper/2606.30442/) (2026)

## Abstract (arXiv)

Configuring the Linux kernel to meet specific requirements, such as binary size, is highly challenging due to its immense complexity-with over 15,000 interdependent options evolving rapidly across different versions. Although several studies have explored sampling strategies and machine learning methods to understand and predict the impact of configuration options, the literature still lacks a comprehensive and large-scale dataset encompassing multiple kernel versions along with detailed quantitative measurements. To bridge this gap, we introduce LinuxData, an accessible collection of kernel configurations spanning several kernel releases, specifically from versions 4.13 to 5.8. This dataset, gathered through automated tools and build processes, comprises over 240,000 kernel configurations systematically labeled with compilation outcomes and binary sizes. By providing detailed records of configuration evolution and capturing the intricate interplay among kernel options, our dataset enables innovative research in feature subset selection, prediction models based on machine learning, and transfer learning across kernel versions. Throughout this paper, we describe how the dataset has been made easily accessible via OpenML and illustrate how it can be leveraged using only a few lines of Python code to evaluate AI-based techniques, such as supervised machine learning. We anticipate that this dataset will significantly enhance reproducibility and foster new insights into configuration-space analysis at a scale that presents unique opportunities and inherent challenges, thereby advancing our understanding of the Linux kernel's configurability and evolution.