Test data
Test data is input, state, fixtures, or supporting information prepared or selected to exercise and evaluate software behavior under specified conditions.
Software Testing with Generative AI
Test data is the input and supporting information provided to software under test to exercise specified behaviors and conditions.
- How it is used
- Supports automated, exploratory, performance, and other testing activities by supplying realistic, synthetic, transformed, or anonymized inputs.
- How deep it goes
- Explains prompt-driven generation, transformation, seeding, and validation, but stops short of fully automating human judgment and verification.
- Examples
- Uses JSON room records, delimited field specifications, SQL seed files, large booking sets, and HTTP message payloads.
- What you can do after
- Build it from scratch, transform it, and integrate it into automated checks using prompts, SQL seed files, scripts, or APIs.
Read how Software Testing with Generative AI explains test data →
BDD in Action, Second Edition
Test data is data prepared or managed to establish conditions for executing and verifying software tests.
- How it is used
- Uses prepared data to place systems in known states and make acceptance-test behavior verifiable.
- How deep it goes
- Explains focused scenario data, persona storage, automated setup, hooks, and separation from UI selectors.
- Examples
- Uses hotel searches, Frequent Flyer credit tables, named personas, JSON-like files, databases, and automated environment setup.
- What you can do after
- Apply it via personas and hooks to initialize precise test states and maintain readable, reusable automated acceptance tests.
Everyday Data Visualization
Test data is deliberately created or collected data used to exercise a system or develop a visualization before real data is available.
- How it is used
- Uses safely generated data to build and test visualization prototypes before production data becomes available.
- How deep it goes
- Explains generation, locating, and connecting data, while deferring any single definitive method because technology stacks vary.
- Examples
- Uses survey responses, button clicks, fake payments, database records, API output, and one-time exports as concrete examples.
- What you can do after
- Build prototypes with data shaped like the eventual production data and connect them to a source or export.
Related concepts
- test data generationproduced by · Test data generation is the creation of input data or fixtures according to defined rules for exercising and validating software.
- prototypingused by · Prototyping is the practice of creating and evaluating a preliminary or partial implementation to explore or validate an idea, design, or approach.
- generative modelingproduced by · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
- promptproduced by · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
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