Course Details
Course Code: – ;Â Â Duration: 2 Days; Instructor-led
To strengthen participants’ testing fundamentals, requirement analysis, test design and risk-thinking capabilities, while enabling them to use AI effectively to support testing without over-relying on AI-generated outputs.
Audience
Business Analysts, QA/Testers, Product Owners and other non-developer roles involved in defining requirements, validating solutions and ensuring software quality.
Prerequisites
None
Methodology
This program will be conducted with interactive lectures, PowerPoint presentation, discussions and practical exercise
Course Objectives
To strengthen participants’ testing fundamentals, requirement analysis, test design and risk-thinking capabilities, while enabling them to use AI effectively to support testing without over-relying on AI-generated outputs.
Outlines
Module 1: Software Testing Fundamentals for Non-Developers
- Purpose and principles of software testing
- Understanding the Software Testing Life Cycle (STLC)
- Role of BA, QA and Product Owner in software quality
- Testing levels and common testing types
- Understanding defects, failures and expected behaviour
- What makes a good test?
- Human judgement vs AI-generated testing
Participants Gain: Understand the fundamentals needed to make better testing and quality decisions.
Module 2: Requirement Analysis, Test Design & Risk-Based Testing
- Turning business requirements and user stories into testable conditions
- Identifying unclear, incomplete and ambiguous requirements
- Breaking requirements into testable scenarios
- Identifying business risks and potential failure points
- Designing positive, negative and edge-case scenarios
- Essential test design techniques for non-developers
- Prioritising testing based on business risk and impact
- Determining appropriate test coverage
Participants Gain: Translate requirements into meaningful tests and focus testing effort on areas that matter most.
Module 3: Test Pyramid & Smart Testing Strategy
- Understanding the Test Pyramid in simple terms
- Unit, Integration and End-to-End testing — what each is for
- What BA, QA and Product Owners need to know about each level
- Understanding where a requirement should be validated
- Avoiding over-reliance on End-to-End testing
- Manual vs automated testing
- Deciding where AI can support the testing process
- Communicating testing expectations with development teams
Participants Gain: Understand what should be tested, where it should be tested and why, without needing to write code.
Module 4: Using AI Across the Testing Lifecycle
- Where AI can support BA, QA and Product Owner activities
- Using AI to analyse and challenge requirements
- Breaking down requirements for better AI results
- Generating test scenarios and test cases
- Discovering negative scenarios and edge cases
- Using AI to identify potential requirement gaps
- Generating test data and testing ideas
- Prompt engineering for testing
- Providing context, constraints and expected outcomes
- Improving AI responses through iterative prompting
Participants Gain: Use AI to accelerate requirement analysis and testing while maintaining control over the process.
Module 5: Human-in-the-Loop — Validating AI-Generated Outputs
- Why AI output should not be accepted blindly
- Setting expected outcomes before asking AI
- Evaluating prompts and AI responses
- Reviewing AI-generated test scenarios
- Checking alignment with business requirements
- Identifying missing coverage and overlooked risks
- Detecting assumptions, inaccuracies and hallucinations
- Applying business knowledge and human judgement
- Improving AI-generated tests before acceptance
Participants Gain: Confidently challenge, validate and improve AI-generated outputs instead of simply trusting them.
