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What Turns Vague Employee Login Notes Into Step-by-Step Use Cases With Testable Criteria?
Learn how generative AI converts disorganized stakeholder input on employee portals into structured use cases and acceptance criteria for payroll access, streamlining systems analysis for development teams.
Question
A stakeholder you’re working with explains that employees must log in to a company portal, view their payroll history, and download pay stubs. Their feedback is scattered and not well organized.
What is one way generative AI can support you in producing use cases and acceptance criteria from this input?
A. It can generate general project goals that focus on company growth strategies without addressing system interactions or user tasks.
B. It can outline step-by-step use cases for employee login and payroll access and generate acceptance criteria that define when each feature is complete and working correctly.
C. It can provide a list of random payroll facts that are unrelated to system requirements or user interactions with the portal.
D. It can produce creative stories about employee experiences that do not include any technical steps or business rules for the system.
Answer
B. It can outline step-by-step use cases for employee login and payroll access and generate acceptance criteria that define when each feature is complete and working correctly.
Explanation
Generative AI supports analysts in producing use cases and acceptance criteria from scattered stakeholder feedback about employee portal features—like login, payroll history viewing, and pay stub downloads—by first extracting key actors, preconditions, and flows through natural language analysis, then structuring them into detailed use cases with step-by-step scenarios (e.g., “Use Case: View Payroll History – Actor: Employee, Precondition: Successful login, Main Flow: 1. Navigate to Payroll tab, 2. Select date range, 3. Display records”) paired with precise, testable acceptance criteria such as “Given employee is logged in, When selecting pay period, Then history loads in under 3 seconds with accurate totals.”
This transforms vague descriptions into traceable, implementation-ready artifacts that cover main paths, alternatives, exceptions, and postconditions, allowing development teams to verify functionality while enabling analysts to spot gaps like security rules or error handling for stakeholder validation. Building directly on this exam’s recurring themes of unstructured input to structured outputs—like user stories from meeting notes or HR system requirements—this method ensures comprehensive coverage, prioritization, and alignment without extraneous content.