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Duration 7 hours
Course Outline
Introduction to AI in Requirements Engineering
- Overview of AI tools available for product teams
- The significance of requirements in Agile and Scrum contexts
- Advantages and constraints of using AI for capturing requirements
Collecting and Organizing Requirements with AI
- AI-driven interview simulations: converting spoken input into requirements
- Prompting strategies to clarify vague statements
- Categorizing requirements into themes and features
Creating User Stories and Epics
- Converting raw text into actionable user stories
- Utilizing AI to identify actors, actions, and objectives
- Building epics and story hierarchies based on AI suggestions
Drafting Acceptance Criteria and Edge Cases
- Formulating testable Given-When-Then criteria
- Detecting exception paths and boundary conditions with AI
- Evaluating AI outputs for clarity and thoroughness
Refining and Grooming Stories with AI
- Condensing notes from stakeholder meetings
- Dividing and combining stories through guided prompts
- Streamlining backlog refinement with AI assistance
Collaboration and Handover
- Distributing AI-generated stories to developers
- Maintaining traceability from features to test cases
- Preparing documentation for stakeholder approval
Summary and Future Directions
Requirements
- Fundamental knowledge of software project lifecycles
- Familiarity with Agile or Scrum methodologies
- No 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
