Testing
Testing RabbitMQ code falls into two categories: unit tests that mock the AMQP connection and verify your business logic, and integration tests that spin up a real broker to confirm routing, acknowledgements, and dead letter queues work correctly. Testcontainers handles the real broker — no manual Docker setup required. Key Takeaways
Python
testbook bridges the gap between notebook exploration and software engineering rigor. While nbmake verifies that a notebook executes without error, testbook lets you write pytest test functions that call specific functions defined in a notebook, inject test data, and assert outputs—the same level of testing you'd apply
Python
Reproducibility is the foundation of scientific computing. A notebook that produces a result should produce the same result when re-executed—same inputs, same code, same environment. nbval enforces this by re-running a notebook and comparing each cell's output to the recorded output in the .ipynb file. Any difference
Python
nbmake is a pytest plugin that treats Jupyter notebooks as test cases. Install it, point pytest at a directory of notebooks, and every notebook that executes without error passes. Every notebook that raises an exception fails. It is the simplest possible notebook testing setup, and for most teams it is
Python
Jupyter notebooks are the primary artifact for data science work—model training, data exploration, report generation. Yet most teams treat them as write-only documents. Notebooks break silently when dependencies update, when data shapes change, or when code paths that ran during development are skipped during the final run. Testing notebooks
Python
Papermill is a tool for parameterizing and executing Jupyter notebooks programmatically. It was designed for production notebook workflows—running the same analysis against different datasets, date ranges, or configuration sets—but it also makes notebooks significantly more testable. By injecting parameters and capturing outputs, you can test a notebook against
Python
mutmut is the most widely adopted Python mutation testing tool. It works by making small, targeted modifications to your source code — flipping + to -, changing True to False, removing return values — and running your test suite against each modification. If your tests don't fail when the code changes,
Python
Documentation lies. Not because developers intend to mislead, but because code evolves and docstrings do not. A function gets refactored, its return format changes, and somewhere in the codebase an outdated >>> example claims it returns a list when it now returns a generator. The user copies that
Python
Testing async Python code requires an event loop to run coroutines. pytest-asyncio and anyio provide that infrastructure and make async tests look nearly identical to synchronous ones. The Problem with Async Tests You can't call await outside a coroutine, so this doesn't work: # Won't
Temporal
Temporal activities are where your workflow touches the outside world — HTTP calls, database writes, email sends. Testing workflows in isolation means replacing activities with mocks that return predictable values without making real external calls. Why Mock Activities Activities in Temporal are the "impure" part of the system — they
Fuzz Testing
Hypothesis is the most accessible entry point to property-based and fuzz testing for Python developers. Unlike AFL++ or libFuzzer which require C/C++ code and complex toolchains, Hypothesis works with any Python function, integrates with pytest, and produces minimal counterexamples automatically. It's been finding real bugs in production
Testing
Testing pandas code requires different patterns than testing application code. DataFrames are complex objects—you rarely want to assert on the entire DataFrame at once. Instead, assert on specific columns, row counts, data types, and statistical properties. Use pytest fixtures for reusable test data, pandas.testing for DataFrame comparisons, and