AI-Driven Software Development Lifecycle (SDLC) Training Course
The AI-Driven Software Development Lifecycle (SDLC) is a practical course designed to demonstrate how artificial intelligence can optimize every stage of the software development process. From automating requirement analysis to generating intelligent tests and optimizing deployment, participants will learn to integrate AI tools and techniques throughout the entire lifecycle.
This instructor-led live training, available both online and onsite, is tailored for intermediate-level software leaders looking to modernize their SDLC with AI-assisted workflows and tools.
By the conclusion of this training, participants will be able to:
- Utilize AI to transform business inputs into structured requirements and user stories.
- Employ LLMs to enhance code documentation, review processes, and refactoring efforts.
- Automate test case generation and conduct coverage analysis using AI tools.
- Implement AI-driven monitoring and decision-making within CI/CD pipelines during deployment.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training session for this course, please contact us to make arrangements.
Course Outline
AI in the Requirements and Planning Phase
- Using NLP and LLMs for requirement analysis.
- Converting stakeholder input into epics and user stories.
- Utilizing 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.
- Validating design through prompt-based system reasoning.
AI-Enhanced Development Workflows
- AI-assisted code generation and boilerplate scaffolding.
- Refactoring code and improving performance 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.
- Generating exploratory and boundary cases with AI.
Documentation, Review, and Knowledge Sharing
- Automatically generating documentation from code and APIs.
- Automating code reviews using AI prompts and checklists.
- Creating knowledge bases and FAQs using conversational AI.
AI in CI/CD and Deployment Automation
- Optimizing pipelines and implementing risk-based testing with AI.
- Providing intelligent suggestions for canary releases and rollbacks.
- Using AI for deployment verification and post-deployment analysis.
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI use and preventing bias in generated code.
- Auditing and ensuring compliance in AI-assisted workflows.
- Developing a roadmap for phased AI adoption across the SDLC.
Summary and Next Steps
Requirements
- A solid 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.
Need help picking the right course?
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AI-Driven Software Development Lifecycle (SDLC) Training Course - Enquiry
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
Michal Maj - XL Catlin Services SE (AXA XL)
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