Automation regression testing saves time, but the datasets that drive it are a bottleneck. Real test data is expensive to acquire, difficult to reproduce, and often too narrow to catch edge cases. When a regression suite relies on a handful of production snapshots, you are testing against a tiny slice of reality, and that slice is constantly changing.
The gap between test coverage and real-world risk
Most regression test suites cover a few dozen workflows: login, invoice generation, basic reporting. That might be enough for happy-path validation, but it leaves wide swaths of the application untested. In large enterprise systems, thousands of user actions exist outside the test matrix. Real data must be collected from production, labeled, and curated, slow work that rarely scales to the full breadth of the UI.
Why real test data falls short for automation regression
Real-world data introduces dependencies. An invoice might only exist after a purchase order is created; a report might only render for users with a specific role. When those conditions aren’t met, the test fails, not because the automation logic is wrong, but because the data didn’t support the expected path. This leads to brittle tests and noisy flakiness. Additionally, extracting clean, structured inputs from production snapshots is labor-intensive, and maintaining that pipeline is hard.
Synthetic data can plug the coverage gap
Synthetic data for automation regression starts with real interactions. By running computer-use agents on real desktops and browsers, teams capture the sequence of clicks, form inputs, and state changes that users actually perform. These sessions become source material for synthetic datasets. You can then generate new sessions that follow similar patterns but cover different branches: error states, edge cases, permission changes, or alternate workflows. The result is a scalable test data set that doesn’t require ongoing production extraction.
Concrete benefits with numbers
A common challenge is that test suites run only a fraction of the possible UI paths. In many large applications, the actual number of distinct workflows exceeds 10,000. Real test data can realistically cover only a few dozen. Synthetic data can expand that coverage dramatically by generating new trajectories that mirror realistic behavior. Teams report being able to test dozens of additional edge cases per sprint without adding more manual test cases. Regression runs also become more stable because synthetic paths are isolated from production changes and don’t introduce external dependencies.
Synthetic data for regression testing is not about replacing real data entirely. It’s about filling coverage gaps, isolating test environments, and reducing flakiness by providing repeatable, controlled inputs that align with how users actually interact with the system.
How Coasty fits
Coasty runs computer-use agents on real desktops and browsers to capture realistic interaction data. That raw footage serves as the foundation for custom synthetic datasets and trajectories tailored for training and evaluating agents and models. Coasty’s offering is a custom synthetic data service: you talk to the team about your use case, and they build the dataset you need. There’s no self-serve portal and no fixed packages, you define the scope, and they deliver.
If you’re struggling with limited regression coverage or noisy automation tests, synthetic data built from real user sessions can give you the breadth and stability you need. To explore how Coasty can help you build a custom synthetic dataset for your automation regression testing, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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