Play 32
AI-Powered Testing
Medium✅ Ready
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
Azure OpenAI (gpt-4o)GitHub ActionsContainer AppsAzure Monitor
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
Coverage targets (80%→95%)Mutation rulesFramework configs (Jest, pytest, xUnit)Test depth per typeParallel execution limits
Estimated Cost
Dev/Test
$50–150/mo
Production
$500–2K/mo