Troubleshooting Tests

Introduction

Troubleshooting failed tests is a critical part of maintaining a robust testing pipeline—unless your tests never fail, in which case, hats off!

Before troubleshooting, you need to detect test failures, ideally via notifications from your CI/CD tools or collaboration platforms. Once a failure is identified, you’ll need access to:

• Logs/Reports/Artifacts: Generated by the testing tool.
• Component Logs/Traces: From the systems under test to help diagnose issues.
• Infrastructure Metrics: To address performance or network-related concerns.

Key Challenges in Traditional CI/CD Environments

Collecting the necessary data in a traditional CI/CD setup can be daunting because:

• Diverse Data Sources: Different testing tools produce various types of logs and artifacts.
• Custom Integration Needs: Efficient analysis often requires custom scripting or additional tools for searching and comparison.
• Manual Log Collection: Gathering logs from the systems under test is typically a manual process that relies on log aggregation or APM tools. Identifying the affected components and the correct time window can be challenging.
• Infrastructure Metrics: Similarly, pinpointing the relevant performance data may require extra effort.

Digging into Logs and Artifacts

After collecting the necessary artifacts, effective troubleshooting generally involves:

• Filtering Noise: Extracting the relevant information from the general “noise” in your logs.
• Error Identification: Searching for specific error codes or messages.
• Comparative Analysis: Comparing current logs or results with previous (successful) runs to identify changes.

Troubleshooting Tests with Testkube

Testkube simplifies the troubleshooting process with several built-in features:

• Automated Collection:
  Automatically gathers logs and artifacts from your test executions and makes them available in the Testkube Dashboard.

• JUnit Report Capture:
  If your testing tool outputs standard JUnit reports, Testkube will capture those for further analysis.

• Enhanced Log Analysis:
  The Dashboard offers features like Keyword Highlighting and Log Comparison to help you identify relevant content quickly.

• AI Insights:
  The AI Insights module leverages GPT to analyze and interpret test logs, providing actionable insights.

Further Reading

For more details on analyzing your test results, read about Analyzing Test Results.