Role-Based Programme
RB1512

AI-Powered Software Testing & Quality Assurance

Smarter Test Design, Defect Analysis & Quality Validation

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Software Testing and Quality Assurance across requirement analysis, test design, defect management, regression planning, and reporting.
  • Explore practical prompting techniques for test scenarios, test cases, edge cases, defect summaries, and QA documentation.
  • Apply AI to organise testing information, identify coverage gaps, improve test documentation, and support defect analysis.
  • Identify opportunities to improve QA productivity, test coverage, documentation consistency, and release-readiness visibility.
  • Recognise data privacy, software-security requirements, test validation, quality governance, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted Test Case DesignRequirement AnalysisDefect SummarisationTest Data SupportRegression PlanningQA DocumentationBasic Test Automation Support

Who should attend

  • Software Test Engineers
  • QA Engineers
  • Quality Assurance Analysts
  • Manual Testers
  • Automation Test Engineers
  • Test Analysts
  • Test Leads
  • QA Leads
  • Software Quality Engineers
  • UAT Professionals
  • Application Testing Professionals
  • Quality Engineering Professionals
  • Software Testing Managers

Prerequisites & Participant Readiness

  • Basic understanding of software testing or quality-assurance concepts
  • Familiarity with requirements, test cases, defects, or software-development activities is helpful
  • Basic computer and software-application knowledge
  • No AI or programming knowledge required
  • No previous Generative AI experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to Software Testing and Quality Assurance
  • Identifying AI applications across requirements review, test design, defect analysis, documentation, and reporting
  • Understanding AI assistance versus Tester and QA professional judgement
  • Recognising activities where approved requirements, expected behaviour, and human validation remain mandatory
Practical activities
  • Mapping common testing and QA activities to potential AI applications
  • Comparing a traditional testing task with an AI-assisted approach

Scenarios

Requirement to Test Coverage

Business Requirement → AI-Assisted Analysis → Test Conditions → Positive & Negative Scenarios → Edge Cases → Test Cases → Coverage Review

Participants use AI to convert a sample software requirement into structured test scenarios and test cases, then review the output for completeness, traceability, and practical execution.

Failed Test to QA Release Summary

Test Failure → Evidence → AI-Assisted Defect Report → Severity & Impact Review → Retest Status → Regression Impact → QA Summary

Participants use AI to organise a sample failed test into a clear defect report, identify relevant regression considerations, and prepare a concise QA status summary while retaining final quality and release decisions with authorised stakeholders.

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