Start with the product and regression risk before reviewing code. A technically elegant framework can still deliver little value when it automates the wrong scenarios.
1. Coverage and priority
Map automated scenarios to business-critical workflows, release risk, execution frequency, and known regression areas. Identify important coverage that still depends entirely on manual validation.
2. Framework structure
Review fixtures, configuration, reusable components, abstractions, test data, environment handling, and how contributors are expected to add new tests. Look for unnecessary complexity as well as duplication.
3. Reliability
Inspect synchronization, locator quality, retries, environment instability, data dependencies, and failure diagnostics. A high pass rate means little if failures are routinely ignored or rerun until green.
4. Maintainability
Look at how often tests require changes for ordinary product updates, whether setup is repeated, and whether the suite makes intent easy to understand.
5. CI/CD value
Determine what runs on pull requests, deployments, scheduled regression, and release gates. Execution time and feedback timing matter as much as total test count.
6. Testability and dependencies
Automation problems are sometimes application problems. Unstable environments, inaccessible test data, weak APIs, or poor observability can make a good framework unreliable.
Expected output
A useful assessment ends with a prioritised backlog: what to preserve, what to improve, which scenarios to automate next, and which product or delivery dependencies need support from engineering.