Home Events AI-Augmented SDLC for .NET Systems with Measurable Outcomes

AI-Augmented SDLC for .NET Systems with Measurable Outcomes

Description

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.

 

🕒 Duration: 8 hours

👥 Target Audience:

  • Roles: Software Engineers/ Architects (.NET, Backend, Full stack), DevOps / SRE Engineers, CTOs / Engineering Managers

 

  • Seniority: Mid-Senior
Webinar Content

 

Module 1: SDLC Reality & AI Opportunity Friction Points in .NET Systems Role-based AI Adoption Current SDLC bottlenecks and AI opportunities
  • 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
Module 2: AI in .NET Development ASP.NET Core Acceleration Test Generation & Refactoring AI-assisted backend development
  • 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
Module 3: Controlled Adoption & Governance Secure AI Usage SDLC Integration Governance and enterprise-safe AI adoption
  • 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
Module 4: Measurement, KPIs & ROI Coding Agents Overview AI-Augmented Workflow Measuring value and introducing agents
  • 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 (%)
Learning Objectives:

After attending this webinar 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
Prerequisite Knowledge
  • Basic experience with .NET (ASP.NET Core) and SQL Server
  • Familiarity with software development workflows (Git, APIs, testing)

 

Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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