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Duration 7 hours
Course Outline
Introduction to AI in Requirements Engineering
- Survey of AI tools available to product teams
- The role of requirements within Agile and Scrum frameworks
- Advantages and constraints of AI in capturing requirements
Collecting and Organizing Requirements via AI
- Simulating interviews with AI to convert spoken input 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
- Leveraging AI to pinpoint actors, actions, and objectives
- Building epics and story hierarchies based on AI suggestions
Drafting Acceptance Criteria and Edge Cases
- Generating Given-When-Then testable criteria
- Detecting exception paths and boundary conditions using AI
- Evaluating AI outputs for clarity and completeness
Refinement and Backlog Grooming with AI
- Condensing stakeholder meetings and notes
- Breaking down or combining stories through prompt guidance
- Streamlining backlog refinement with AI assistance
Collaboration and Handover
- Distributing AI-generated stories to developers
- Maintaining traceability from feature to test case
- Preparing documentation for stakeholder approval
Recap and Future Actions
Requirements
- Fundamental knowledge of software project lifecycles
- Basic familiarity with Agile or Scrum methodologies
- No prior technical expertise 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
