CI Failure Tracker Dashboard

Open source web dashboard for monitoring, analyzing, and tracking CI test failures in OpenShift. Features detailed failure metrics, test history, and export capabilities.

Features

Test Failure Tracking

• Real-time dashboard with test failure metrics
• Historical tracking (configurable time range)
• Platform-specific failure rates (AWS, Azure, GCP, vSphere, Nutanix)
• Version comparison across OpenShift releases
• Comprehensive test statistics and trends

Interactive Test Analysis

• Click any test to view detailed failure information
• View error logs with syntax highlighting
• Filter failures by platform
• Timestamp and job information for each failure
• Direct links to Prow CI jobs

Data Export

• Export test results in XLSX, CSV, or Markdown formats
• Version and time-range filtering
• Platform-specific sheets/sections
• Latest test run status (excludes skipped tests)
• Automated filename generation with metadata

Quick Start

Prerequisites

• Python 3.10+
• Access to OpenShift CI (for data collection)

Installation

1. Clone the repository:

git clone https://github.com/redhat-community-ai-tools/ci-dashboard-tracker.git
cd ci-dashboard-tracker

2. Install dependencies:

pip install -r requirements.txt

3. Configure your jobs in config.yaml:

collector:
  type: "reportportal"  # or "prow_gcs"
  
  reportportal:
    url: "https://your-reportportal-instance.com"
    project: "your-project"
    # API token from environment variable: REPORTPORTAL_API_TOKEN
    job_patterns:
      - "periodic-ci-openshift-COMPONENT-release-{version}-*"

4. Collect test data:

python dashboard.py collect --days 30

5. Start the web server:

python dashboard.py serve

6. Open http://localhost:8080 in your browser

Configuration

Edit config.yaml to customize:

• Job patterns: Which CI jobs to monitor
• Versions: OpenShift versions to track
• Platforms: Cloud platforms to monitor
• Time range: How many days of history to collect
• Blocklist: Tests to exclude from dashboard

See config.yaml for detailed configuration options.

Data Collectors

The dashboard supports multiple data collection methods:

• reportportal: ReportPortal API (requires authentication)
• prow_gcs: Direct GCS bucket access (requires credentials)

AI-Powered Failure Analysis (Optional)

The dashboard includes AI-powered test failure analysis using Claude via Google Vertex AI. This feature provides root cause analysis, component identification, and suggested actions for test failures.

Setup Vertex AI

1. Enable Vertex AI API in your Google Cloud project:

   gcloud services enable aiplatform.googleapis.com

2. Set environment variables:

   export ANTHROPIC_VERTEX_PROJECT_ID="your-gcp-project-id"
   export ANTHROPIC_VERTEX_REGION="global"  # or "us-east5" for regional

3. Authenticate with Google Cloud:

   gcloud auth application-default login

4. Install the Anthropic SDK with Vertex support:

   pip install 'anthropic[vertex]'

Using AI Analysis

Once configured, click the "AI Analyze" button on any failed test in the dashboard to get:

• Root cause analysis
• Affected component identification
• Classification (product bug, automation issue, infrastructure problem, etc.)
• Platform-specific failure patterns
• Suggested remediation actions
• JIRA-ready issue descriptions

Cost: Approximately $0.02 per test analysis using Claude Sonnet.

Note: The dashboard only supports Vertex AI for AI analysis. Direct Anthropic API access is not configured.

Deployment

Local Development

python dashboard.py serve --port 8080

OpenShift Deployment

See openshift/ directory for deployment manifests.

Basic deployment:

oc apply -f openshift/

Usage

Collect Data

# Collect last 30 days
python dashboard.py collect --days 30

# Collect specific version
python dashboard.py collect --days 7 --version 4.22

# Collect specific platform
python dashboard.py collect --days 14 --platform aws

Export Data

Access exports via the web UI or API:

curl "http://localhost:8080/api/export?format=xlsx&days=7&version=4.22" -o export.xlsx

Customization for Your Team

This dashboard is designed to be customized for any OpenShift testing team:

1. Update config.yaml with your job patterns
2. Modify tracking.test_suite_filter to match your test suite
3. Add your blocklist of tests to exclude
4. Customize platforms and versions

Example for Storage team:

collector:
  reportportal:
    job_patterns:
      - "periodic-ci-*-storage-*"
      - "periodic-ci-*-csi-*"

tracking:
  test_suite_filter: "Storage"
  platforms:
    - "aws"
    - "azure"
    - "gcp"

Architecture

• Data Storage: SQLite database
• Web Framework: Flask
• Frontend: Vanilla JavaScript with modern CSS
• Data Collection: Pluggable collectors (ReportPortal, Prow GCS)
• Export: OpenPyXL for Excel, CSV, Markdown

Contributing

Contributions welcome! This is an open source project maintained by the OpenShift QE community.

License

Apache 2.0

Support

For issues and questions, please open a GitHub issue.