CST438 Week 5

This week's reading from Software Engineering at Google covered large tests. The main takeaway is that larger tests trade speed and simplicity for fidelity. Unlike unit tests, they test how the system actually behaves in conditions closer to production, which means they catch things unit tests simply cannot. The tradeoff is that they are slower, more expensive to run, and harder to maintain.

What stuck with me was that larger tests still use mocks in some cases, and that Google does not rely on full automation scripts for everything. Two key ingredients for large tests to work well are realistic seed data and production data, which makes sense because a test is only as useful as how closely it mirrors real usage.

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