Features
AI-assisted requirements authoring with scoring across five quality dimensions
Automatic requirement enhancement and rewrite for clarity and completeness
AI test case generation covering positive, negative, boundary, and edge scenarios
Automatic executable test script generation from test cases without coding
AI agent-based test execution with automatic retries and artifact collection
Log, screenshot, and video capture during test execution
End-to-end traceability matrix linking requirements to tests to results
Real-time coverage and quality dashboards with drill-down reports
Native Jira integration for import/export of requirements and results
Native Azure DevOps integration for requirement import and result push
Batch execution of test suites across multiple environments
Shift-left testing support from requirement stage
Audit-ready traceability for enterprise compliance
AI-powered impact analysis for regression test prioritization
Self-healing automation with context from Jira and bug history
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
