New Release: AI-Powered Testing & In-App Support

AI-Powered Test Actions & Smart Locators

Writing reliable E2E tests just got easier. We've introduced AI-based action values and semantic locators that understand your UI contextually, not just structurally. Instead of brittle CSS selectors that break on every UI refactor, our AI analyzes your page content and generates stable, meaningful locators.

• Dynamic AI Action Values: The test agent now infers input values, button labels, and navigation paths based on your application's actual data—eliminating hardcoded test data that becomes stale.
• Semantic Locators: AI identifies elements by their purpose and context, making tests self-healing and resistant to DOM changes.
• Loading Detection: Automatically waits for async operations to complete, reducing flaky tests caused by race conditions.

Business value: Reduce test maintenance time by up to 60% and eliminate flaky tests that slow down your CI/CD pipeline. Perfect for teams adopting shift-left testing who need developer confidence without sacrificing delivery velocity.

Automatic Check Suggestions from HAR Analysis

Quality shouldn't be an afterthought. Our new HAR analyzer watches your test runs and automatically proposes production-grade checks based on real API traffic. It extracts response schemas, identifies authentication patterns, detects JWT tokens, and suggests validation rules—all without manual setup.

• Zero-Config Check Discovery: Analyze HAR files from test runs to auto-generate API checks for status codes, response structure, and headers.
• JWT & Auth Detection: Automatically identifies OAuth tokens and suggests response header validation for security-critical endpoints.
• Deduplicated Suggestions: Smart tracking prevents duplicate check recommendations across test runs.

Why it matters: Shift-left testing means catching production bugs before deployment. With automated check suggestions, your QA team can expand test coverage 3x faster, aligning with DORA metrics for continuous deployment readiness.

Slack-Integrated In-App Support

Support shouldn't slow you down. We've built a native support chat directly into the desplega.ai dashboard that connects to our internal Slack workspace. Your questions reach our team instantly—no context-switching, no email threads, no delays.

• Real-Time Chat: Send messages, share screenshots, and get responses without leaving the app.
• Persistent Conversations: All support threads are saved and searchable, so you never lose important context.
• Internal Notifications: Our team gets instant Slack alerts, ensuring fast response times during critical debugging sessions.

ROI for CTOs: Faster support resolution means less downtime for your QA engineers. When testing pipelines break, instant support access translates to reduced Mean Time to Recovery (MTTR)—a key DORA metric.

Global Variables & Configuration

Managing environment-specific test data across dozens of checks is painful. We've introduced system-wide global variables that cascade across all tests, suites, and CI runs. Define your staging URLs, API keys, or test user credentials once, and they're available everywhere.

• Organization-Level Variables: Set global defaults that apply to all tests in your workspace.
• Secure Storage: Encrypted variable storage keeps sensitive credentials safe.
• Override Flexibility: Test-specific variables can override globals when needed.

This eliminates configuration drift and reduces onboarding time for new team members—critical for startups scaling their QA processes.

Playwright CI Reporter Enhancements

CI/CD observability is essential for tracking test health across sharded runs. Our Playwright reporter now captures CI metadata, shard indices, and run IDs from GitHub Actions, CircleCI, and other providers. This enables better correlation of test results across parallel CI jobs.

• CI Environment Detection: Automatically extracts build numbers, branch names, and commit SHAs from 10+ CI providers.
• Sharding Support: Track test results across parallelized runs with shard correlation.
• HTTP Batching: Improved reporter performance with request batching and Redis-backed state management.

Impact on DORA metrics: Better CI observability means faster root-cause analysis when tests fail, directly improving deployment frequency and change failure rate.

UI/UX Polish

We've refined the dashboard navigation with a redesigned sidebar that groups features logically and improves discoverability. Plus, an auto-login page for seamless authentication in development environments, and smarter test run filtering by CI run IDs and shard totals.

Performance & Reliability

Behind the scenes, we've improved test execution stability with better loading detection, URL validation warnings (instead of blocking saves), and migration scripts for organizations moving data between environments. These changes reduce test flakiness and make desplega.ai more resilient at scale.