geo-infer-test

You can run automated tests across multiple GEO-INFER modules using a unified test runner that executes unit, integration, and performance tests with pytest to validate code quality and system integrity.

Validating module structure and test coverage before deployment is best handled by a module health check framework that verifies dependencies, test file quality, and ecosystem-wide statistics using pytest-cov and custom validation tools.

Yes, pytest-benchmark and locust are supported dependencies for executing performance tests within a CI/CD pipeline, allowing you to validate system performance and identify regressions across all modules.

Property-based testing with hypothesis and data validation using geopandas are fully supported dependencies, enabling comprehensive validation of geospatial data structures and dynamic test case generation.

Generating test reports and checking ecosystem-wide statistics is achieved using pytest-html and jinja2 dependencies, which produce detailed HTML outputs validating test file quality and module health across 44 packages.

Memory profiling with memory-profiler and load testing with locust on complex geospatial modules may face resource constraints or timeouts, requiring pytest-timeout configurations to prevent CI/CD pipeline stalls during extensive validation.