Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Artificial Intelligence Applications in Requirements and Planning
- Leveraging natural language processing and large language models for requirement analysis
- Translating stakeholder inputs into epics and user stories
- Utilizing AI tools to refine user stories and generate acceptance criteria
AI-Augmented Design and Architecture
- Employing AI to model system components and dependencies
- Generating architecture diagrams and Unified Modeling Language (UML) suggestions
- Validating design decisions through prompt-based system reasoning
AI-Enhanced Development Workflows
- Supporting code generation and creating boilerplate scaffolding with AI assistance
- Improving code refactoring and performance using large language models
- Integrating AI tools into Integrated Development Environments (IDEs), such as Copilot, Tabnine, and CodeWhisperer
Testing with AI
- Generating unit and integration tests using AI models
- Facilitating regression analysis and test maintenance through AI assistance
- Identifying exploratory and boundary cases with AI support
Documentation, Review, and Knowledge Sharing
- Automatically generating documentation from code and application programming interfaces (APIs)
- Automating code reviews using AI prompts and checklists
- Developing knowledge bases and frequently asked questions (FAQs) via conversational AI
AI in Continuous Integration/Continuous Deployment (CI/CD) and Automation
- Optimizing pipelines and conducting risk-based testing with AI enhancement
- Providing intelligent suggestions for canary releases and rollback procedures
- Supporting deployment verification and post-deployment analysis using AI
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code for government applications where appropriate
- Maintaining auditing standards and compliance within AI-assisted workflows
- Developing a roadmap for phased AI adoption across the software development life cycle
Summary and Next Steps
Requirements
- Proficiency in software development lifecycle methodologies
- Background in software architecture or team leadership roles
- Knowledge of DevOps principles, agile frameworks, or SDLC tools
Target Audience
- Software architects
- Development team leads
- Engineering management personnel
14 Hours
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