# An Evolutionary Study of Configuration Design and Implementation in Cloud Systems

arXiv: https://arxiv.org/abs/2102.07052 · HTML: https://mlsanity.com/paper/2102.07052/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [Unicorn: Reasoning about Configurable System Performance through the lens of Causality](https://mlsanity.com/paper/2201.08413/) (2022)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [Configuration Validation with Large Language Models](https://mlsanity.com/paper/2310.09690/) (2023)
- [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)
- [Towards automation of threat modeling based on a semantic model of attack patterns and weaknesses](https://mlsanity.com/paper/2112.04231/) (2021)
- [Can LLMs Configure Software Tools](https://mlsanity.com/paper/2312.06121/) (2023)

## Abstract (arXiv)

Many techniques were proposed for detecting software misconfigurations in cloud systems and for diagnosing unintended behavior caused by such misconfigurations. Detection and diagnosis are steps in the right direction: misconfigurations cause many costly failures and severe performance issues. But, we argue that continued focus on detection and diagnosis is symptomatic of a more serious problem: configuration design and implementation are not yet first-class software engineering endeavors in cloud systems. Little is known about how and why developers evolve configuration design and implementation, and the challenges that they face in doing so. This paper presents a source-code level study of the evolution of configuration design and implementation in cloud systems. Our goal is to understand the rationale and developer practices for revising initial configuration design/implementation decisions, especially in response to consequences of misconfigurations. To this end, we studied 1178 configuration-related commits from a 2.5 year version-control history of four large-scale, actively-maintained open-source cloud systems (HDFS, HBase, Spark, and Cassandra). We derive new insights into the software configuration engineering process. Our results motivate new techniques for proactively reducing misconfigurations by improving the configuration design and implementation process in cloud systems. We highlight a number of future research directions.