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Duration 7 hours
Course Outline
Foundations of AI in Requirements Engineering
- Survey of AI tools for product teams
- The function of requirements within Agile and Scrum
- Advantages and constraints of AI in requirement capture
Capturing and Organizing Requirements with AI
- AI-driven interview simulations: converting verbal feedback into requirements
- Prompting strategies to resolve ambiguous statements
- Structuring requirements into themes and features
Creating User Stories and Epics
- Converting raw text into executable user stories
- Utilizing AI to pinpoint actors, actions, and objectives
- Building epics and story hierarchies from AI insights
Defining Acceptance Criteria and Edge Cases
- Developing Given-When-Then testable criteria
- Detecting exception paths and boundary conditions with AI
- Evaluating AI outputs for clarity and completeness
Refining and Grooming Stories with AI
- Condensing stakeholder meeting notes and discussions
- Splitting and combining stories with prompt guidance
- Streamlining backlog refinement with AI assistance
Team Collaboration and Handoff
- Distributing AI-generated stories to development teams
- Maintaining traceability from features to test cases
- Preparing documentation for stakeholder approval
Recap and Future Directions
Requirements
- Foundational knowledge of software project lifecycles
- Familiarity with Agile or Scrum methodologies
- No prior technical experience needed
Intended Audience
- Product owners
- Business analysts
- Scrum masters
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