TestOptimizer

TestOptimizer: Accelerating QA with Smart Test Prioritization In modern software development, large test suites often slow down CI/CD pipelines, delaying feedback and deployment. For the Kyiv QA Automation Summit 2025 Hackathon, we introduce TestOptimizer, a Python-based tool that optimizes test execution by prioritizing critical tests and parallelizing them across available resources. Aligned with the theme "Smarter Testing for a Smarter Future," it tackles the pain point of inefficient test runs head-on. TestOptimizer works by analyzing Git diffs to identify code changes, then uses a test-to-code mapping and historical data (failure rates, execution times) to prioritize tests. Tests tied to modified files or with a history of failures run first, while faster tests are favored for quick wins. The tool then splits the prioritized list into parallel chunks, leveraging tools like pytest-xdist to execute them concurrently. The result? A dramatic reduction in test suite runtime—potentially halving it for large projects—without compromising coverage. Built for practicality, TestOptimizer integrates with CI/CD systems like GitHub Actions or Jenkins. QA engineers provide a simple YAML config mapping code files to tests, and the tool outputs a JSON execution plan with prebuilt pytest commands. For example, if src/login.py changes, TestOptimizer might prioritize test_login and test_auth, splitting them across four CI nodes for parallel execution. This ensures critical feedback arrives fast, while less relevant tests don’t clog the pipeline. The innovation lies in its intelligent prioritization and scalability. Unlike static test runners, TestOptimizer adapts to each code change, using data-driven insights to focus effort where it matters most. It’s lightweight (no heavy ML required), easy to set up (just Python and a Git repo), and extensible—future versions could integrate with cloud platforms or auto-generate mappings via code analysis. For QA teams, TestOptimizer delivers real-world value: faster pipelines, reduced resource waste, and confidence in test results. A team with a 30-minute suite could see runtimes drop to 10-15 minutes, accelerating delivery in agile environments. By combining automation with smart decision-making, TestOptimizer empowers engineers to keep pace with today’s rapid development cycles, making it a game-changer for efficient, reliable testing.

Stack Language: Python Libraries: gitpython for Git diff analysis pandas for historical data processing pytest for test execution with parallel support (via pytest-xdist) yaml for configuration CI/CD Integration: Works with GitHub Actions or Jenkins Output: Optimized test execution plan (JSON)

Structure

TestOptimizer/
├── README.md
├── requirements.txt
├── src/
│   ├── change_analyzer.py  # Analyzes Git diffs
│   ├── prioritizer.py      # Prioritizes tests based on changes and history
│   ├── parallelizer.py     # Splits tests for parallel execution
│   └── utils.py            # Helper functions
├── tests/
│   ├── sample_tests.py     # Sample test suite
│   └── test_history.csv    # Sample historical test data
└── run_optimizer.py        # Entry point script

Built With

• pandas
• pytest
• python