Testing at Scale
Testing a single function is one thing; gaining confidence in a whole program is another. As programs grow, testing organizes into levels.
A unit test checks one small piece — a single function or module — in isolation, with all its dependencies controlled. Unit tests are fast, pinpoint failures precisely (you know exactly which unit broke), and form the base of the testing pyramid. An integration test checks that several units work correctly together — that the interfaces between modules actually fit. A module can pass every unit test yet fail in integration because two modules disagree about their shared interface. At the top, system (end-to-end) tests exercise the whole program as a user would.
Test coverage measures how much of the code the tests exercise (for example, what fraction of lines or branches run during testing). High coverage is valuable — untested code is unverified — but coverage is necessary, not sufficient: running a line is not the same as checking that it behaves correctly. Coverage tells you what was touched, not what was validated.
Two enabling practices: automated tests can be re-run cheaply after every change, and a good suite acts as a regression safety net — it catches when a change breaks previously-working behavior. Many teams even write tests first (test-driven development), letting the tests specify the behavior before the code is written.
Common pitfall: treating high code coverage as proof of correctness. Coverage only measures which code ran during tests, not whether each result was actually checked or every edge case exercised. A suite can hit 100% coverage while still asserting almost nothing meaningful — coverage is a floor, not a guarantee.