RPA teams know the pain: to test a bot you need a sandbox with valid credentials, real contracts, and a stable environment. Running regression tests on live systems risks leaking sensitive data, and spinning up fresh environments for every test is expensive and slow. Synthetic data helps you train and evaluate bots without ever touching real production environments.
Real sessions are hard to reuse
Many automation teams capture sessions in production to build training data. The problem is that production logs contain sensitive fields: names, addresses, contract IDs, and internal IDs. You cannot safely re-use that raw footage in a regression pipeline. On top of privacy concerns, session behavior drifts over time, UI changes, new error messages, and workflow updates invalidate old recordings quickly. Teams end up with a small pool of brittle, high-risk data that barely covers the current release.
How synthetic test sessions work
Synthetic data generation for automation starts with a model of the workflow. You describe the steps: login, navigate to the portal, upload a file, fill a form, submit, and log out. The system then fabricates realistic inputs and interaction sequences. It doesn’t just pick random strings for text fields, it respects format constraints, mimics user pacing, and follows the visual layout of the UI. For file uploads, synthetic test files can match the expected MIME types and size ranges without being real documents. This approach produces thousands of unique session trajectories that behave like production traffic but contain no real PII or confidential data.
Coverage and confidence
A recent benchmark for enterprise automation testing showed that synthetic test suites achieved 92% coverage of the core workflow in a single generation cycle. When the team compared synthetic regressions against manual test runs, they found false positives dropped by 60% because the synthetic sessions accurately reflected error states and edge cases. Another study measured the stability of synthetic test suites over six months of production releases: the same dataset remained valid for 80% of updates, reducing test maintenance effort by half. These numbers come from independent research, not vendor marketing campaigns, and they highlight how synthetic data can tighten regression feedback loops.
The main takeaway: synthetic test sessions let you run regression tests at scale without exposing production data or rebuilding environments for every run, giving you faster feedback and higher confidence in your automation pipelines.
How Coasty fits into RPA regression testing
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This capability lets teams describe a workflow and receive synthetic sessions that behave like real users without ever touching production systems. The service is custom and contact-led: you talk to the Coasty data team to define requirements, data formats, and coverage goals. There are no fixed packages or public pricing, each engagement is tailored to your regression needs and compliance boundaries.
If you want to build synthetic test suites for RPA regression without risking real data, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to explore what’s possible for your automation pipeline.
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