Summary Fern Platform addresses the challenge of fragmented test data by providing a unified test intelligence platform. It aggregates results from diverse CI/CD pipelines and testing frameworks, offering real-time analytics and AI-powered insights. This enables engineering teams to identify flaky tests, monitor performance, and proactively prevent failures, thereby improving test suite health and reliability.

How It Works The platform employs a domain-driven design with a hexagonal architecture, exposing data through a REST API for ingestion and a GraphQL API for querying. It centralizes test results, automatically detecting flaky tests and tracking performance trends. This specialized approach offers deeper, actionable insights into test suite health compared to general-purpose analytics tools, with future plans for AI-driven predictive capabilities.

Quick Start & Requirements Installation is recommended via Kubernetes, requiring Docker with buildx, k3d, kubectl, Go 1.21+, and Make. After cloning the repository and configuring host entries (127.0.0.1 fern-platform.local, 127.0.0.1 keycloak), deployment is initiated with make deploy-all, taking approximately 15 minutes. Access is available at http://fern-platform.local:8080 using default credentials admin@fern.com / test123. Docker images are planned for future release. Official client libraries exist for JavaScript/Jest, Java/JUnit, and Go/Ginkgo, with guides available for building custom clients.

Highlighted Details

• Universal Test Aggregation: Ingests test results from any framework or CI/CD system via a REST API.
• Flaky Test Detection: Automatically identifies tests exhibiting intermittent pass/fail behavior.
• Performance Monitoring: Tracks test execution times to pinpoint performance regressions.
• GraphQL API: Enables complex querying and analysis of historical test data.
• AI-Powered Insights: Future enhancements include root cause analysis, predictive failure detection, and natural language querying.

Maintenance & Community Fern Platform is under active development, with core features considered stable and production-ready. Contributions are welcomed, particularly for client libraries, framework integrations, UI/UX improvements, documentation, and bug fixes. Community interaction is facilitated through GitHub Discussions for questions and ideas, and the Issue Tracker for bug reports and feature requests.

Licensing & Compatibility The project is licensed under the Apache License 2.0, permitting broad use and modification, including for commercial purposes, without the copyleft restrictions of licenses like GPL.

Limitations & Caveats Docker images are not yet available, with Kubernetes deployment being the current primary installation method. Advanced AI-powered intelligence features are still in development and not yet released.