Have filters tested automatically

Filters are the workhorses of modern web applications. They allow users to quickly narrow down vast amounts of information—whether it's products, articles, or events—to what is relevant to them. However, this powerful feature is extremely prone to errors, especially as complexity increases. What happens when a user combines multiple filters from different categories? Does the displayed number of results match the actual count? Are events that have already passed correctly hidden? For one of our clients, the reliability of their filter function was absolutely business-critical, and we secured it with a multi-layered, automated test.

The challenge was the sheer complexity and the resulting need for absolute consistency. The system allowed users to freely combine various filters, for example by topic, location, and date. The number of resulting hits was displayed in two different places in the user interface: once directly by the filter options and again prominently above the results list. Even the slightest discrepancy between these two counts, or with the actual number of articles displayed, would immediately undermine user trust and make the entire application look unprofessional.

Furthermore, the underlying business logic—such as the correct handling of event dates—had to be accurate at all times. Manually testing all of this in every conceivable combination was simply impossible and was like trying to empty an ocean with a teaspoon.

Our task was to develop a test that would comprehensively and reliably validate not just the filters themselves, but the entire ecosystem of filter logic, result counting, and data timeliness.

We developed a test scenario that verifies the reliability of the filter function from multiple perspectives at once, thereby ensuring absolute data integrity.

The core of the test is a rigorous, three-way validation of the result count that goes far beyond a simple check. After our script applies a defined filter combination, it performs a three-way reconciliation that leaves no room for inconsistency. First, it reads the displayed result count directly beside the filter options. Immediately after, it captures the second displayed count located prominently above the results list. Finally, in the crucial third step, it independently counts the articles or events that are actually rendered on the page. This part of the test is only considered successful if all three values—the two displayed counts and the real number of items—are an exact match. This rigorous method relentlessly uncovers any possible inconsistency between the back-end logic and the front-end display.

Furthermore, the test addresses the business logic of the filters, because a filter must not only work technically but also correctly implement business rules. We validated this using the critical example of the date filter. The test selected the date filter and then iterated through every single event in the results list. For each event, it verified that the event date was either in the future or the current day. This ensured that no outdated, past events were displayed—a decisive quality feature for content relevance and user satisfaction.

Finally, to cover the entire user workflow, the test verifies the complete functional cycle. It not only tests the application of filters but also the critical functionality of the "Reset Filters" button, verifying that the correct, unfiltered full view is displayed afterward.

The result is the highest level of confidence in one of the client's most central functions. The development team has the assurance that the filter logic and its corresponding result display will remain consistent and correct, even after significant changes to the system. Embarrassing display errors or the presentation of irrelevant, outdated content are proactively prevented before they can reach a user. The test ensures a reliable and professional user experience, which is essential for customer retention and the brand's image.

This use case is a prime example of the TESTIFY approach. We look beyond the obvious function to analyze the underlying rules and dependencies. Based on this foundation, we build tests that focus not just on the technology, but on functional correctness and data consistency, thereby safeguarding the true value of an application.

Is the reliability of your filter and search functions also critical to your business? We develop precise test scenarios that ensure your users always see the right results.