AI-Driven Quality Engineering, Powered by Real Application Context
Our AI Framework goes beyond traditional automation — using MCP to feed real-time DOM, logs, traces, network events, and user interactions directly into the AI reasoning engine.
Model Context Protocol (MCP):
The Missing Piece in AI-Based Testing
AI alone cannot test software effectively — it needs context.
MCP provides:
- DOM snapshots
- Network logs
- API requests/responses
- Errors, console logs, traces
- Screenshots & UI metadata
- Test run telemetry
This context allows AI to:
- Understand the application
- Reason about failures
- Fix selectors
- Suggest missing tests
- Predict risks
- Generate actionable insights
This is what makes QAstra different — we give AI the intelligence to understand your product.
How the QAstra AI Framework Works
AUT (Your Application)
MCP Server ←→ Observability Layer (Logs, Traces, Metrics)
AI Reasoning Engine (OpenAI Models)
Self-Healing Layer (Locator + Flow Repair)
Automation Execution Engine (Playwright / Appium)
CI/CD Pipeline (GitHub / Azure)
AI Insights Dashboard
What the AI Framework Enables:
AI-Generated Test Scenarios
AI analyzes flows, MCP telemetry, and usage patterns to propose missing tests and edge cases.
Self-Healing Locators
The AI fixes broken selectors using:
- DOM diffs
- MCP-provided attributes
- Visual context
- Interaction traces
- No more brittle selectors.
Intelligent Failure Classification
Failures get grouped by true root cause, not test outputs:
- Missing wait
- Backend issue
- Selector change
- Visual shift
- Network timeout
- UI regression
- This reduces triage time drastically.
Predictive Risk Detection
AI identifies:
- unstable elements
- frequently failing flows
- high-risk areas before they break
- Powered by historical MCP telemetry.
Visual & DOM AI Comparisons
AI understands the difference between:
- UI redesign
- CSS shifts
- Functional regressions
- Rendering issues
AI-Assisted Debugging
For every failure, AI generates:
- the cause
- recommended fix
- probable owner (frontend/backend)
- suggested locator or retry mechanism
Impact on Your QA & Development Workflow
- Reduce flaky tests by 60–80%
- Fix failures 4× faster
- Deliver 40% more automation with same team
- Identify risks before production
- Massive drop in manual triage work
- Ideal for fast-moving teams with weekly releases
This is the strongest value proposition you can offer.
Where the AI Framework Delivers the Most Value:
- Dynamic UI (React/Angular/Vue)
- Mobile apps with frequent UI updates
- API-first platforms with evolving schemas
- Fast-moving SaaS releases
- E-commerce / finance / insurance flows
- Apps with complex user journeys
- OpenAI Models
- MCP Server
- AI Reasoning Engine
- Playwright
- Appium
- Grafana
- Allure
- GitHub Actions
- Azure DevOps