Quality Assessment and Improvement of Helm Charts for Kubernetes-Based Cloud Applications

TL;DR

This paper evaluates the quality of Helm Charts for Kubernetes, introduces HelmQA for automated assessment, and demonstrates how systematic use of the tool can improve chart quality over time.

Contribution

It provides the first quantified insights into Helm Charts quality and community practices, and proposes a hypothesis-based methodology for quality improvement using HelmQA.

Findings

Identified quality issues in public Helm Charts

HelmQA can systematically detect quality deficiencies

Regular use of HelmQA reduces quality issues over time

Abstract

Helm has recently been proposed by practitioners as technology to package and deploy complex software applications on top of Kubernetes-based cloud computing platforms. Despite growing popularity, little is known about the individual so-called Helm Charts and about the emerging ecosystem of charts around the KubeApps Hub website and decentralised charts repositories. This article contributes first quantified insights around both the charts and the artefact development community based on metrics automatically gathered by a proposed quality assessment tool named HelmQA. The work further identifies quality insufficiencies detectable in public charts, proposes a developer-centric hypothesis-based methodology to systematically improve the quality by using HelmQA, and finally empirically attempts to validate the methodology and thus the practical usefulness of the tool by presenting results of its application over a representative four-month period. Although one of our initial hypotheses does not statistically hold during the experiment, we still infer that using HelmQA regularly in continuous software development would lead to reduced quality issues.