AI-Driven Software Development Lifecycle (SDLC)

• An understanding of software development lifecycle concepts
• Experience in software architecture or team leadership
• Familiarity with DevOps, agile practices, or SDLC tooling

Audience

• Software architects
• Development leads
• Engineering managers

AI-Driven Software Development Lifecycle (SDLC) is a practical course that explores how artificial intelligence can enhance each phase of the software development process. From automating requirement analysis to intelligent test generation and deployment optimization, participants will learn how to integrate AI tools and techniques across the full lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software leaders who wish to modernize their SDLC with AI-assisted workflows and tools.

By the end of this training, participants will be able to:

• Leverage AI for converting business input into structured requirements and user stories.
• Use LLMs to improve code documentation, review, and refactoring.
• Automate test case generation and coverage analysis using AI tools.
• Apply AI-driven monitoring and deployment decision-making in CI/CD pipelines.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI in the Requirements and Planning Phase

• Using NLP and LLMs for requirement analysis
• Converting stakeholder input into epics and user stories
• AI tools for story refinement and acceptance criteria generation

AI-Augmented Design and Architecture

• Using AI to model system components and dependencies
• Generating architecture diagrams and UML suggestions
• Design validation through prompt-based system reasoning

AI-Enhanced Development Workflows

• AI-assisted code generation and boilerplate scaffolding
• Code refactoring and performance improvements using LLMs
• Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)

Testing with AI

• Generating unit and integration tests using AI models
• AI-assisted regression analysis and test maintenance
• Exploratory and boundary case generation with AI

Documentation, Review, and Knowledge Sharing

• Automatic documentation generation from code and APIs
• Code review automation using AI prompts and checklists
• Creating knowledge bases and FAQs using conversational AI

AI in CI/CD and Deployment Automation

• AI-enhanced pipeline optimization and risk-based testing
• Intelligent canary release and rollback suggestions
• AI in deployment verification and post-deploy analysis

Governance, Ethics, and Implementation Strategy

• Ensuring responsible AI use and avoiding bias in generated code
• Auditing and compliance in AI-assisted workflows
• Building a roadmap for phased AI adoption across SDLC

Summary and Next Steps