Kubernetes Configuration Defect

Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors.Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical study aboutconfiguration defects in Kubernetes with the goal of helping practitioners detect and prevent these defects. We study 719 defects thatwe extract from 2,260 Kubernetes configuration scripts using open source repositories. Using qualitative analysis, we identify 15categories of defects. We find 8 publicly available static analysis tools to be capable of detecting 8 of the 15 defect categories. We findthat the highest precision and recall of those tools are for defects related to data fields. We develop a linter to detect two categories ofdefects that cause serious consequences, which none of the studied tools are able to detect. Our linter revealed 26previously-unknown defects that have been confirmed by practitioners, 19 of which have already been fixed. We conclude our paper byproviding recommendations on how defect detection and repair techniques can be used for Kubernetes configuration scripts. Thedatasets and source code used for the paper are publicly available online.Kubernetes是一款可助力软件快速部署的工具。但遗憾的是，Kubernetes的配置过程极易出现错误。配置缺陷并非罕见，且可能引发严重后果。本文针对Kubernetes中的配置缺陷开展了一项实证研究，旨在帮助从业者检测并防范此类缺陷。我们从开源仓库的2260份Kubernetes配置脚本中提取得到719处缺陷，并将其作为本次研究的分析样本。通过定性分析，我们共识别出15类配置缺陷。我们发现，现有8款公开可用的静态分析工具（static analysis tools）仅能覆盖15类缺陷中的8类。此外，这些工具对与数据字段相关的缺陷，其查准率和查全率均处于最高水平。我们开发了一款代码检查工具（linter），用于检测两类会引发严重后果，但现有研究工具均无法识别的缺陷。该工具共发现26处此前未被知晓的缺陷，经从业者确认后，其中19处已完成修复。本文最后针对如何将缺陷检测与修复技术应用于Kubernetes配置脚本，给出了相应的建议。本文所用的数据集与源代码均已在线公开。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于Kubernetes配置缺陷的实证研究，基于2,260个配置脚本提取了719个缺陷，并归类为15个类别。研究评估了现有工具仅能检测部分缺陷，并开发了新的linter工具，成功发现了26个先前未知的缺陷，为Kubernetes配置的缺陷检测和预防提供了数据支持和实践建议。