python-unit-test

User Input

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Goal

Create Python unit tests focused on logic, edge cases, and regressions for simulation and research modules.

Bundled assets

• templates/test_module_template.py

Workflow

1. Detect active framework (pytest or unittest) and folder conventions.
2. Map critical functions/classes and error paths in the target module.
3. Create parameterized tests and mock external dependencies (MQTT, file I/O).
4. Run target tests and verify failure output.
5. Report incremental coverage and uncovered cases.

Simulation & Research Test Focus Areas

• DES engine: Verify event ordering, causal correctness, deterministic replay with same seed.
• Station/process logic: Test processing times, resource acquisition, exception paths (jams, failures).
• Distribution sampling: Test that seeded distributions produce reproducible outputs.
• Entity lifecycle: Test creation, state transitions, completion, and discard paths.
• Resource constraints: Test capacity enforcement, queue discipline, blocking behavior.
• Belief updates: Test monotonic refinement — feasible set never expands without new evidence.
• Supervisor constraints: Test that only supervisor-enabled actions are selected.
• Config loading: Test YAML/JSON config parsing, schema validation, default values.
• Event logging: Test that monitor produces correct CSV output without altering simulation state.
• Metric computation: Test metric aggregation, warm-up exclusion, per-seed reproducibility.

Rules

• If pytest is present, prefer it.
• Do not depend on network or real filesystem except for dedicated fixtures.
• Use descriptive test names and single responsibility per test.
• Always test with reproducible seeds — never use unseeded randomness in tests.
• Mock MQTT connections — never connect to live brokers in tests.
• Test that identical seed + config produces identical event logs.