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Duration 7 hours
Course Outline
Introduction to AI in Requirements Engineering
- Survey of AI tools available for product teams
- The significance of requirements within Agile and Scrum
- Advantages and constraints of AI in requirement capture
Collecting and Organizing Requirements with AI
- Simulated interviews with AI: converting verbal input into requirements
- Prompting strategies to resolve ambiguous statements
- Categorizing requirements into themes and features
Creating User Stories and Epics
- Converting plain text into actionable user stories
- Utilizing AI to identify actors, actions, and goals
- Constructing epics and story hierarchies from AI recommendations
Drafting Acceptance Criteria and Edge Cases
- Producing Given-When-Then testable criteria
- Detecting exception paths and boundary conditions via AI
- Evaluating AI outputs for clarity and completeness
Refinement and Story Grooming with AI
- Condensing stakeholder meetings and notes
- Dividing and combining stories using prompt guidance
- Streamlining backlog refinement with AI assistance
Collaboration and Handover
- Distributing AI-generated stories to developers
- Maintaining traceability from feature to test case
- Producing documentation for stakeholder approval
Summary and Future Steps
Requirements
- A foundational grasp of software project lifecycles
- Familiarity with Agile or Scrum frameworks
- No prior technical background is necessary
Target 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
