# Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs

arXiv: https://arxiv.org/abs/2306.07653 · HTML: https://mlsanity.com/paper/2306.07653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis](https://mlsanity.com/paper/2509.04191/) (2025)

## Abstract (arXiv)

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.