FERC — Data Entry Automation
Background
The Federal Energy Regulatory Commission (FERC) relies on its Data Entry system to process, validate, and publish filings received from multiple integrated applications. Once validated, these records are made available through eLibrary WebSearch for internal teams and the public. Given the scale and mission-critical nature of the workflow, dependable validation and timely publishing were essential to operational continuity.
Challenge
The Data Entry system had to verify a high volume of scenarios and complex field rules, which made comprehensive manual testing increasingly difficult within release timelines. Regression cycles demanded six weeks of concentrated effort by multiple testers, slowing quarterly deployments and limiting the ability to conduct on-demand validation. Preparing test data itself required significant lead time, often consuming a full week before testing could begin. Given the extensive number of scenarios and compressed release schedules, testing efforts were necessarily selective, which limited coverage of some critical paths.
Solution
Our team implemented a comprehensive automation approach tailored to the Data Entry workflow. The framework enabled unattended, overnight regression runs with complete reporting available by morning, removing the dependency on manual oversight for repetitive tasks. It expanded coverage to include all scenarios and validation paths, ensuring consistency across complex scenarios that previously could only be sampled. Automated test data generation dramatically accelerated preparation, allowing QA teams to initiate validation within hours rather than days. The solution also enabled on-demand execution throughout release windows, supporting rapid re-tests when changes were introduced and improving overall confidence in deployments.
Results
- Regression testing reduced from six weeks to eight hours
- Test data preparation reduced from seven days to a few hours
- Comprehensive coverage achieved across numerous validation scenarios
- On-demand execution enabled multiple full regression cycles per release
- QA effort shifted from repetitive validation to higher-value analysis and oversight