Test Execution Engine & Reliability Improvements

When a test fails, you need to know why—fast. We've completely rebuilt how we track test execution to give you crystal-clear visibility into what happened. Now when a test fails, you can see exactly which step passed, which step failed, how many times we retried it, and how long each step took. This detailed execution history makes debugging test failures dramatically faster.

The improved execution tracking works seamlessly in your CI/CD pipeline for automated runs, and also supports step-by-step debugging when you need to investigate issues interactively. You can pause after any step, inspect the browser state, check what data was captured, and continue—giving you complete control over test execution when you need it most.

For teams running extensive end-to-end testing suites, this means less time spent investigating failures and more time shipping features. Instead of guessing why a test failed, you get a complete timeline showing exactly what happened at each step. This directly improves developer velocity by eliminating the frustrating "why did this fail?" debugging sessions that slow down your continuous deployment workflow.

Flaky tests are the bane of every developer's existence. A test passes locally but fails in CI/CD pipeline—was it a real bug or just a network hiccup? We've built intelligent retry logic that automatically handles transient failures. When a test step fails due to timing issues, slow page loads, or network glitches, the system automatically retries with smart delays, dramatically reducing false test failures.

The retry system is smart about what to retry. Safe operations like navigation or assertions get automatic retries, while destructive actions (like deleting test data) are never retried to prevent data corruption. Each retry is tracked so you can see exactly what happened—how many retries were needed, how long the system waited between attempts, and whether the test eventually passed or genuinely failed.

For teams battling flaky tests, this is transformative. Instead of your CI/CD pipeline failing on transient issues that resolve themselves, the system intelligently retries and recovers automatically. Tests that pass after retries are flagged as potentially flaky, giving you visibility into reliability issues before they cause production bugs. This means fewer interrupted deployments, less time investigating false positives, and more developer confidence in your test results. Combined with our DORA metrics tracking, you can see how many failures were automatically recovered versus real regressions, giving you cleaner signals about when it's safe to deploy.

When AI generates test data or extracts information from pages, you need confidence that the data is correct. We've added automatic validation that checks AI-generated data against your expected formats before tests run. This catches errors like wrong data types, missing fields, or invalid formats immediately—before they cause cryptic test failures deep in execution.

The validation system works with any data structure you define, from simple values to complex nested objects. When validation fails, you get clear error messages showing exactly what went wrong and which fields need fixing. This makes it easy to debug AI-generated data issues and iterate on your AI test automation workflows.

For teams using AI testing capabilities, this validation layer provides crucial safety. Instead of discovering that AI generated malformed data halfway through a test run, you catch these issues immediately with actionable error messages. This improves developer confidence in AI-generated tests by ensuring data quality from the start. For QA for startups building sophisticated test automation, this enables complex workflows where AI extracts data from pages, transforms it, and feeds it into subsequent tests—all with automatic quality checks.

When a test fails, you shouldn't have to guess why. We've significantly improved our automatic failure analysis to tell you exactly what went wrong. The system analyzes error messages, browser state, screenshots, and execution history to automatically classify failures into clear categories: application bugs, test script issues, environment problems, third-party service failures, or timing issues.

Instead of manually investigating every failure, you get instant insights like "This looks like a third-party API timeout" or "This selector doesn't match any element—possible UI change". The system also distinguishes between flaky tests (failures that resolve on retry) and genuine regressions (consistent failures), so you know what needs immediate attention versus what can wait.

For teams managing large test suites, this dramatically reduces triage time. Critical application bugs trigger immediate notifications, while transient infrastructure issues are logged without alert fatigue. This accelerates mean time to recovery, a key DORA metrics indicator. For scaling QA operations, this enables self-service failure analysis where developers can diagnose test failures without QA team intervention, improving developer velocity and reducing bottlenecks.

AI shouldn't be a black box. We've launched a dedicated view that shows you exactly how our AI agents work when they help generate tests, debug issues, or resolve problems. You can see every step the AI took, what information it used, which tools it called, and how it reached its conclusions. This transparency builds trust and makes AI assistance more useful.

When an AI-generated test doesn't work as expected, you can review the complete reasoning process: Did it find the right documentation? Did it understand your requirements correctly? What decisions did it make along the way? This visibility enables rapid iteration—you can see what worked, what didn't, and improve your prompts and workflows accordingly.

For teams leveraging E2E testing AI capabilities, this transforms AI from a mysterious helper into a transparent, debuggable tool. For shift-left testing practices where developers create tests alongside code, having clear visibility into AI assistance builds confidence and enables more sophisticated workflows. The session view also helps with knowledge transfer—new team members can see how the AI approaches problems, learning testing best practices by example.

We've streamlined the workflow for turning exploratory test sessions into formal automated tests. When you discover a critical user flow during manual exploration, you can now promote it into a regression test with clearer status indicators, better validation, and smoother UI feedback throughout the process.

If something prevents promotion (like missing configuration), you get clear, actionable error messages explaining exactly what needs to be fixed. These improvements reduce friction in test creation, making it easier for teams to scale their test automation coverage.

For teams practicing continuous deployment, this accelerates the feedback loop. Product teams can explore new features manually, and when they discover critical flows, immediately promote those explorations into regression tests without waiting for QA to write tests from scratch. This embodies shift-left testing—testing happens earlier and more iteratively, catching issues before they reach production. Better promotion UX means more tests get created, increasing coverage and reducing the risk of regressions. Combined with our self-healing tests capabilities, promoted tests remain maintainable even as your UI evolves.

We'd love to hear how these reliability improvements impact your testing workflow! Does better failure diagnosis save you time? Are fewer flaky tests improving your developer confidence? Reach out at contact@desplega.ai or schedule a demo to see these improvements in action and discuss your testing needs!