Testing .NET Apps Built with AI

Blazor applications generated by AI frequently suffer from component lifecycle bugs. OnInitializedAsync methods that do not handle cancellation, event callbacks that cause infinite re-renders, and component parameters that lose their values during rendering cycles are common problems.

Blazor Server apps have the additional challenge of SignalR circuit management. AI tools generate code that does not handle circuit disconnections gracefully, leaving users with frozen interfaces when their connection drops momentarily. Blazor WebAssembly apps may have excessive download sizes because AI tools include unnecessary assemblies.

Testers interact with every Blazor component, testing interactive features, form submissions, and real-time updates. They notice when components stop responding, when state is lost unexpectedly, and when the UI does not reflect the current data.

Entity Framework Core configurations generated by AI often include incorrect relationship mappings, missing indexes, and migration scripts that fail in production. Lazy loading may be misconfigured, causing either N+1 queries or null reference exceptions when navigating relationships.

ASP.NET Core API controllers frequently lack proper model validation, return incorrect status codes, and do not handle concurrent requests correctly. AI tools also generate controllers that expose more data than intended, returning full entity graphs instead of DTOs.

Testers verify that all data operations work correctly through the application interface. They create, read, update, and delete records, checking that the correct data appears on every page and that operations complete without errors.

Submit your deployed .NET application and testers will cover the full stack. They test Razor Pages, MVC views, Blazor components, and API endpoints. They verify that Identity authentication works correctly, that authorization policies are enforced, and that SignalR hubs deliver real-time updates.

Testers also check deployment-specific issues like incorrect environment variable handling, missing configuration transforms, and health check endpoint behavior. These issues are common in AI-generated .NET apps that work perfectly in development but fail in production environments.