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I spent more time on fake data because the tests needed more than values that looked believable. They needed records that fit the schema, obeyed business rules, connected to one another, covered useful scenarios, and produced the same result when a test ran again. Building that reliable setup became its own engineering task.
What “fake data” meant in my tests
“Fake data” can describe several different things, and they solve different problems. A fixture is a specific set of values for a test. A test double replaces a dependency, such as a service, with controlled behavior. A data generator supplies varied field values. Factories assemble objects, often with relationships. A seed script loads records into a database. Synthetic data is generated to resemble characteristics of real data, while masking alters real data to obscure sensitive values.
Those categories overlap in everyday conversation, but they are not interchangeable. A generated name does not make a realistic database scenario; a fake service does not populate a database; and masking a dataset does not, by itself, establish that it is safe to share.
Why test data took more effort than it first appeared
Believable fields were not enough
A row generator can fill every column and still produce a record that could never exist in the application. A useful scenario may need to respect foreign keys, uniqueness, allowed values, null behavior, date ordering, and valid state transitions. Related records must agree with one another, too. Randomly generating each field in isolation can create combinations that production code never encounters—or combinations that violate assumptions hidden in the code.
The challenge is less about making a name or address look real and more about encoding the rules that make the whole scenario meaningful. Software Engineering Daily identifies unrealistic event sequences as one fake-data anti-pattern: data can have plausible individual values but an impossible order of events. Software Engineering Daily’s discussion of fake-data anti-patterns gives that distinction useful context.
The tests needed scenarios, not just rows
Test data has to exercise behavior. A happy-path fixture may be enough for one focused test, while another test needs an empty result, a boundary value, a duplicate, an expired record, or a sequence of related events. The right cases depend on what the code is supposed to do; a large dataset is not automatically better coverage.
That made setup decisions important. I had to decide which details were essential to the assertion, which were incidental, and whether a test belonged at a unit, integration, or end-to-end level. The CDS Handbook recommends pushing data complexity down the test pyramid where possible: lower-level tests can use controlled data or mocks, while higher-level tests should include only the realism they need. The CDS Handbook’s test-data guidance covers these trade-offs.
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Randomness made failures harder to repeat
Generated values can add variety, but uncontrolled randomness makes a failure difficult to reproduce: the next run may not contain the same input. Deterministic fixtures and fixed seeds, where supported, help keep tests repeatable. When a generator produces a failure, capturing or logging the generated values makes it possible to investigate the exact case. The CDS Handbook specifically advises recording generated values when a test fails.
Schema changes made broad setup brittle
Every broadly reused fixture or seed script can carry assumptions about the schema and application rules. When those change, old setup can fail—or worse, keep passing while no longer representing a meaningful scenario. The CDS Handbook advises keeping necessary seed scripts minimal, version-controlled, and idempotent, meaning they can run repeatedly without creating duplicate or inconsistent data. Seeding should be used when a test genuinely needs it, not as the default answer to every setup problem.
Choosing the right approach for each test
| Approach | Best fit | Main trade-off |
|---|---|---|
| Explicit fixture | A small, exact, readable scenario for one test. | Predictable and easy to inspect, but can become verbose or stale when copied widely. |
| Fake or other test double | A unit or component test that needs a controlled dependency and known behavior without contacting a remote service. | Isolates the behavior under test; replacing dependencies is harder when construction is not under test control. |
| Faker-style values | Producing varied field values such as names or addresses without typing each one by hand. | Saves repetitive setup, but randomness must be controlled or captured to reproduce failures. |
| Object factory | Creating related domain objects with readable setup. | Useful for relationships and reusable construction, but adds another layer to maintain. |
| Seeded or synthetic relational dataset | Integration, end-to-end, analytics, or load scenarios that need many connected records. | Can provide scale and relationships, but requires ongoing work to preserve schema fit, constraints, and data quality. |
Android Developers describes a fake as an implementation of an interface that can return known data, which is useful when a test needs a controlled dependency rather than a real network call. The guidance also notes that dependency replacement is easier when construction is under the test’s control. Android’s guide to test doubles explains the approach.
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For varied values, a library such as Faker can eliminate repetitive hand-written fields. When objects must be related, a factory is often a better fit; the CDS Handbook names factory_boy as an option for complex related objects. Neither tool decides which cases matter to the test—that remains part of the test design.
Fake data and synthetic data are not the same thing
In casual use, “fake data” may mean any invented test values. Synthetic data usually refers to generated data intended to retain patterns or characteristics of real data. MIT News quotes Kalyan Veeramachaneni, principal investigator of the Data to AI Lab and a principal research scientist in MIT’s Laboratory for Information and Decision Systems: “Fake data is randomly generated,” says Veeramachaneni. “While synthetic data is trying to create data from a machine learning model that looks very realistic.” MIT News’s explanation of synthetic data provides that distinction.
Best Value
Resemblance is not the same as privacy or fitness for a particular test. A fake name or masked field does not prove that a dataset cannot reveal information about real people. MIT’s discussion cautions that synthetic data derived from real data should not contain or hint at information from its source. Any privacy claim depends on the method and the dataset, not merely on whether values appear synthetic.
For large, relational datasets, specialized tools may claim to preserve relationships or constraints while generating, masking, or subsetting data. Those are vendor-described capabilities, not proof that a particular output satisfies your application’s rules. Synthesized describes these capabilities in its product documentation; teams still need to check generated data against their own schema and scenarios.
What I would do differently next time
- Start from the assertion. Identify the behavior the test must prove, then include only the data needed to make that case clear.
- Use explicit fixtures for small, exact cases. They keep important edge conditions visible instead of burying them in a general-purpose generator.
- Use test doubles at dependency boundaries. When the test needs a known response rather than a live service, a fake can make the behavior controlled and repeatable.
- Use factories when relationships are the repetitive part. Keep defaults small and make unusual states explicit in the test.
- Make generated cases reproducible. Fix seeds where possible, and retain the values that triggered failures.
- Reserve database seeding and large datasets for tests that need them. Keep required scripts minimal, idempotent, and maintained alongside schema changes.
My time went into finding the smallest setup that was both realistic enough to exercise the behavior and controlled enough to explain a failure. That is why the data sometimes demanded more thought than the production code around it: the test setup had to make the application’s assumptions visible rather than merely fill its fields.
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