Test Orchestrator

Overview

Coordinate parallel test execution across multiple test suites, frameworks, and environments. Manages test splitting, worker allocation, result aggregation, and intelligent retry strategies.

Prerequisites

• Test runner with parallel execution support (Jest, Vitest, pytest-xdist, Playwright, or JUnit 5)
• CI/CD platform configured (GitHub Actions, GitLab CI, CircleCI, or Jenkins)
• Test suite with consistent pass rates (flaky tests identified and tagged)
• Sufficient CI runner resources for parallel worker count
• Test result reporting tool (JUnit XML, Allure, or equivalent)

Instructions

1. Analyze the existing test suite using Grep and Glob to catalog all test files, their framework, approximate run time, and dependency requirements.
2. Classify tests into execution tiers:

• Tier 1 (Fast): Unit tests with no I/O -- target under 30 seconds total.
• Tier 2 (Medium): Integration tests requiring local services -- target under 3 minutes.
• Tier 3 (Slow): E2E and browser tests -- target under 10 minutes.

1. Configure parallel execution for each tier:

• Split unit tests across N workers using jest --shard=i/N or pytest -n auto.
• Shard E2E tests by test file using Playwright --shard=i/N or Cypress parallelization.
• Assign heavier integration tests to dedicated workers with more resources.

1. Create a CI pipeline configuration that runs tiers in parallel:

• Tier 1 and Tier 2 run concurrently on separate jobs.
• Tier 3 runs after a fast pre-check gate passes.
• Each tier reports results to a unified collection step.

1. Implement intelligent retry logic for flaky tests:

• Tag known flaky tests with @flaky or equivalent marker.
• Retry failed tests up to 2 times before marking as failed.
• Track flaky test frequency in a log file for triage.

1. Aggregate results from all parallel workers into a single report:

• Merge JUnit XML files from each shard.
• Calculate total pass/fail/skip counts and execution time.
• Identify the slowest tests for optimization targets.

1. Write the orchestration configuration to the project's CI config file and validate it with a dry run.

Output

• CI pipeline configuration file (.github/workflows/test.yml, .gitlab-ci.yml, or equivalent)
• Test sharding configuration with worker count and split strategy
• Merged test result report in JUnit XML or JSON format
• Execution timeline showing parallel job durations and bottlenecks
• Flaky test inventory with retry counts and failure pattern