Not every check needs to be written up as a detailed test case with full steps. Sometimes a checklist is faster and more convenient — simply a list of what needs to be checked. And for any check you need test data: without the right data, even a perfect test case won't catch anything. Let's look at both tools.

Checklist vs test case

A checklist is a list of items — "what to check" — without detailed steps. For example, for a registration form:

  • Registration with valid data
  • Email already taken
  • Empty fields
  • Password too short
  • Invalid email format (missing the "@")
  • Leading and trailing spaces in fields
  • Very long name

The difference from a test case: a test case says how to check (step by step) and what to expect; a checklist only says what to check, and the tester keeps the "how" in their head.

When to use which:

  • Checklist — when the checks are simple and obvious, time is short, and the tester is experienced. Quick to write, quick to run. Ideal for exploratory testing and quick smoke checks.
  • Test case — when the check is complex, exact order matters, other people will run the case, or it will go into a long-lived regression suite. Takes more time to write, but it's unambiguous and repeatable.

In practice a common approach is to start with a checklist (quickly sketch out what to check at all), then expand the most important items into full test cases.

What test data is

Test data is the specific values you plug into your checks: usernames, passwords, amounts, files, text strings. It seems like a small thing, but whether you find a bug or not depends directly on the data you use. A check using the name "Anna" will always pass — but a name with 300 characters, a name with an emoji, or a name with an apostrophe (O'Brien) might well break something.

It's useful to always prepare data of three kinds:

  • Valid — correct, "happy path" data: an ordinary name, a valid email, a normal amount.
  • Invalid — obviously wrong: an empty field, letters instead of numbers, an email without "@", a negative amount.
  • Boundary — at the edges of the allowed range: a name exactly at the maximum length and one character longer, the minimum and maximum amount (see boundary values).

Tricky data people forget

There's a specific set of "awkward" data values that regularly break inexperienced code — worth keeping in mind:

  • Empty and whitespace — an empty field, only spaces, leading and trailing spaces.
  • Very long text — a thousand characters where ten were expected.
  • Special characters — quotes, apostrophes ('), angle brackets (<>), percent signs, emojis.
  • Different languages and cases — Cyrillic, hieroglyphs, ALL CAPS and lowercase.
  • Number traps — zero, a negative number, a very large number, a decimal where an integer was expected.
  • Date traps — February 29th, yesterday and tomorrow, a past date in a "date of birth" field that's supposed to be in the future.

Keep this list handy — it alone turns up a huge number of bugs on perfectly ordinary forms.

Where to get test data

Three common sources:

  • Make it up yourself — enough for simple fields.
  • Take it from the requirements — they often include examples and constraints (maximum length, format).
  • Generate it — for bulk or realistic data there are free generators (search for "test data generator"): random names, addresses, card numbers for testing.

An important rule: never use real personal data of real people for testing. It's both unsafe and often illegal. For checks, use invented or anonymized data.

Where this applies

Checklists and choosing test data are everyday work. A new form arrives — you quickly sketch a checklist of what to check, and prepare valid, invalid, and boundary data for each item. How many bugs you find depends directly on how "nasty" the data you come up with is.

Where beginners stumble:

  • Testing only "Anna" and "anna@example.com" — tidy valid data — and therefore only finding the obvious. Bugs hide in awkward data.
  • Forgetting about special characters, length extremes, and empty fields — the most productive set of data.
  • Writing detailed test cases where a checklist would do and wasting time.
  • Using real people's data for testing — this must not be done.

What to learn next. Test design is covered. The next half of the work: what to do with the problems you find. Start with what a bug is and how to assess its severity.