Why Synthetic Data Is the Missing Ingredient for RPA and Automation Regression Testing

The Hidden Cost of Real Test Data

Real test environments are fragile. A single configuration change breaks dozens of scripts. More importantly, many workflows involve sensitive information, PII, financial records, or regulated content, so teams must mask, de‑identify, or redact data before reusing it. This sanitization is time‑consuming and can introduce subtle bugs. A 2023 study of enterprise automation teams found that 42 % of regression test failures were caused by outdated or misaligned test data, not by code defects. When you cannot safely reuse real logs, you have to rebuild them or live with incomplete coverage.

What Synthetic Data Actually Is

Synthetic data is generated by simulating real user actions. Instead of replaying recorded clicks, you model the underlying decision‑making process, what users see, what they type, which errors they encounter, and how they react. This approach lets you explore edge cases that rarely appear in production: malformed inputs, unusual workflows, or security‑related scenarios. A well‑built synthetic test suite can run in parallel across multiple environments, providing consistent results every time. The key is realism: if the synthetic actions feel too scripted, the test is likely to miss real‑world behaviors.

Concrete Tradeoffs

• Coverage: Synthetic data can generate scenarios that are statistically rare in production, such as network failures, UI glitches, or multi‑step error recovery flows.
• Reproducibility: Every synthetic run is identical, making it trivial to track whether a regression fix actually worked.
• Safety: No production data is ever exposed, simplifying compliance with privacy regulations.
• Speed: Generating synthetic interactions can be orders of magnitude faster than provisioning real desktop or browser environments.
• Maintenance: Synthetic scenarios must be updated whenever the UI or business rules change, which can be a recurring effort.
• Realism gap: Poorly designed synthetic generators may miss domain‑specific nuances, so validation against a small sample of real logs is recommended.

Why Synthetic Data Matters for Regression Testing

Automation regression testing thrives on breadth and depth. You want to test every path through a form, every error message, and every integration point. Synthetic data makes this feasible at scale. For example, a payroll processing workflow that normally sees three error types per hundred runs can be expanded to 10,000 synthetic runs, allowing teams to observe rare but critical failure modes. Synthetic logs also serve as a training set for new automation agents, so your regression suite itself becomes a source of continuous improvement.

How Coasty Fits

Coasty runs computer‑use agents on real desktops and browsers to capture realistic interaction data. This means the synthetic datasets it produces reflect actual UI states, timing, and user choices. Teams can use these captured trajectories as the basis for custom synthetic test suites tailored to their own workflows. The offering is a custom service led by the data team, you discuss your requirements, and they build the right synthetic dataset for your regression needs.