In a fast-paced development environment, change is inevitable. New features roll out, UI elements shift, and workflows evolve to meet user needs. But with change comes an unfortunate byproduct—flaky tests. These unreliable tests are a silent productivity killer, slowing down engineering teams, creating false negatives, and delaying deployments. When your CI/CD pipeline grinds to a halt because of a test failure, the first question isn’t "Did our app break?" but rather "Did our test break?" And that’s a problem.
The Reality of Flaky Tests
End-to-end (E2E) tests are supposed to serve as a safety net, ensuring that changes don’t introduce regressions. However, in dynamic applications, test failures often stem from minor UI changes rather than actual issues in the product. A button moves, a modal gets an extra confirmation step, or a form is split into a wizard—suddenly, your carefully written tests become obstacles instead of safeguards. Developers lose hours debugging false failures, and confidence in automated testing erodes.
At Checksum AI, we recognize that the nature of tests is to break. Instead of treating this as a failure of automation, we built a system that embraces change and intelligently adapts. Our solution focuses on two key areas: fast test execution and intelligent auto-recovery.
Why We Generate Test Code Instead of Running AI on Every Step
Many AI-driven testing solutions attempt to dynamically adjust during every test run by relying on inference models at runtime. The downside? These tests are slow and expensive, as inference loads stack up quickly. Instead, Checksum AI takes a different approach:
We generate test code that runs as fast as native Playwright tests.
This keeps test execution lightweight and cost-effective.
No need for constant AI inference—we only step in when something actually breaks.
Users aren’t locked into using Checksum and at any point, they can decide to run their tests as regular Playwright tests.
How Checksum AI’s Auto-Recovery Works
When UI changes beyond the ability of static test code to handle—or when UX changes break user flows—our runtime environment kicks in. Here’s what happens:
Evaluating the Situation – When a test encounters an unexpected change, our agent analyzes the difference between the expected and actual behavior.
Understanding the Intent – Instead of failing immediately, our system considers what the test was originally designed to do.
Surgical intervention – The agent adapts in real time, applying minimal, targeted corrections to let the test continue.
For example, let’s say you add a new confirmation step before deleting an item. Our agent detects the change, gains control of the test execution, clicks the new confirmation button, and then hands control back to the original test.
Or maybe you decide to break a long form into a multi-step wizard. No problem—our agent fills in the first step, clicks "Next," and continues filling out fields until it can confidently release control back to the test script.
How It Compares With Current Solutions
Some testing solutions offer a form of auto-recovery, often referred to as a fallback mechanism. These typically rely on heuristics or machine learning models that collect static data for each element a test interacts with—such as IDs, classes, attributes, XPath, and alternative selectors. When a test fails to locate an element, these solutions attempt to use that data to find a match. While this approach can sometimes work, it has inherent limitations. First, it focuses solely on element anchoring and cannot handle more complex failures. Second, it struggles in fast-paced development environments where frequent UI changes can quickly render stored data obsolete.
Clear, Actionable Reporting
You don’t just get a “Test Passed” result and wonder what happened. Every auto-recovery action is logged in the test report, so you know exactly what changed and how the agent adapted. This transparency ensures developers and QA teams maintain trust in the process.
Auto-Recovery vs. Auto-Healing
While auto-recovery ensures tests continue running even when minor changes occur, there’s a bigger challenge: how do we actually update failing test code to reflect new user flows? That’s where our auto-healing process comes in. It involves multiple agent passes, test run validations, generalization, and adaptation to produce fully updated test scripts. But we’ll save that deep dive for another post.
Conclusion: Testing That Keeps Up With Development
In modern software development, flaky tests shouldn’t slow you down. By combining AI-driven test generation with intelligent auto-recovery, Checksum AI ensures that tests remain stable even as your product evolves. No more wasted hours debugging brittle scripts—just fast, reliable testing that moves at the speed of development.
If you’re tired of flaky tests slowing down your team, check out Checksum AI - it’s built to help you ship with confidence, no matter how fast things change.
