AI-Augmented SDLC for .NET Systems with Measurable Outcomes
Certificate of Completion by Code.Hub
This course provides a practical and structured approach to integrating AI into the Software Development Life Cycle (SDLC) for .NET-based systems, focusing on controlled adoption, governance, and measurable impact. Participants will explore how AI assistants and coding agents can accelerate development, testing, and operations while maintaining quality, security, and compliance. The course emphasizes real-world use cases in ASP.NET Core, SQL Server, and DevOps pipelines, combined with KPI-driven measurement of productivity and ROI. Through hands-on exercises, learners will implement AI-augmented workflows and evaluate their effectiveness in a controlled engineering environment.
By the end of this module, participants will be able to:
- Integrate AI into .NET SDLC phases with structured governance
- Use AI to accelerate API development, testing, and debugging
- Define and track productivity KPIs and engineering metrics
- Apply secure and controlled AI usage within enterprise environments
- Evaluate ROI and business impact of AI adoption
SDLC Reality & AI Opportunity Friction Points in .NET Systems Role-based AI Adoption
Analysis of .NET SDLC bottlenecks (API delays, testing gaps, documentation lag) Mapping AI use cases per role (Dev, QA, DevOps) AI Practice: Identify a real feature and use AI to suggest improvements in design and implementation
AI in .NET Development ASP.NET Core Acceleration Test Generation & Refactoring
Generate controllers, services, DTOs using AI AI-based refactoring and code explanation Automated unit test generation (xUnit) AI Practice: Generate a full ASP.NET Core endpoint with tests and review output quality
Controlled Adoption & Governance Secure AI Usage SDLC Integration
Prompt libraries and standardization Human-in-the-loop validation and review gates Secure AI usage with Azure OpenAI (data/IP protection) AI integration across SDLC stages AI Practice: Define a prompt template and apply it consistently across multiple development tasks
Measurement, KPIs & ROI Coding Agents Overview AI-Augmented Workflow
Engineering metrics (cycle time, lead time, defect rate) AI-specific KPIs (acceptance rate, time saved) Introduction to coding agents and automation loops AI Practice: Measure time saved in a development task and calculate productivity gain (%)
- Roles:Software Engineers/ Architects (.NET, Backend, Fullstack), DevOps / SRE Engineers ,CTOs / Engineering Managers
- Seniority: Mid-Senior
- Basic experience with .NET (ASP.NET Core) and SQL Server
- Familiarity with software development workflows (Git, APIs, testing)
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
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