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Duration 7 hours
Course Outline
Introduction to AI in Requirements Engineering
- Survey of AI tools available to product teams
- The significance of requirements within Agile and Scrum
- The advantages and constraints of AI in requirement capture
Capturing and Structuring Requirements via AI
- AI-driven interview simulation: converting verbal feedback into requirements
- Prompt engineering strategies to resolve ambiguous statements
- Arranging requirements into thematic groups and features
Creating User Stories and Epics
- Converting raw text into executable user stories
- Leveraging AI to pinpoint actors, actions, and objectives
- Developing epics and story hierarchies based on AI recommendations
Drafting Acceptance Criteria and Edge Cases
- Producing testable criteria using the Given-When-Then format
- Detecting exception paths and boundary conditions with AI support
- Evaluating AI-generated outputs for clarity and thoroughness
Refinement and Story Grooming with AI
- Condensing stakeholder meeting summaries and notes
- Dividing and combining stories using guided prompts
- Streamlining backlog refinement with AI aid
Team Collaboration and Handover
- Distributing AI-created stories to development teams
- Maintaining traceability from features to test cases
- Preparing documentation for stakeholder approval
Recap and Future Directions
Requirements
- A foundational grasp of software project lifecycles
- Familiarity with Agile or Scrum methodologies
- No prior technical experience necessary
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
