Play 32
AI-Powered Testing
Autonomous test generation — unit, integration, E2E, and mutation testing for polyglot codebases.
AI-driven test generation engine that produces unit, integration, E2E, and property-based tests across polyglot codebases. Analyzes code structure to generate meaningful test cases, performs mutation testing to verify test quality, tracks coverage metrics, and integrates directly into CI/CD pipelines via GitHub Actions. Azure OpenAI powers code understanding and test synthesis, Container Apps hosts the engine, and Azure Monitor tracks quality metrics.
Architecture Pattern
AI test generation: code analysis → test synthesis, mutation testing, CI/CD integration
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Testing Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (102 lines), evaluate (104 lines), tune (102 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json — code understanding prompts
- config/testing.json — coverage targets, mutation rules, framework configs
- config/guardrails.json — code execution sandboxing, safety
- evaluation/ — test quality scoring, mutation kill rate
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.