Role-Based Programme
RB1513

AI-Powered Software Testing & Quality Assurance

Smarter Test Design, Defect Analysis & QA Automation

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Software Testing and Quality Assurance across requirement analysis, test design, execution support, defect analysis, and reporting.
  • Apply AI-assisted techniques to generate test scenarios, test cases, validation checklists, test data, and defect summaries.
  • Use structured prompting for functional testing, regression testing, exploratory testing, defect investigation, and QA documentation.
  • Explore AI-supported approaches for improving test coverage, identifying missing scenarios, analysing recurring defects, and accelerating QA workflows.
  • Build responsible AI-assisted software-testing workflows while maintaining test accuracy, traceability, security, privacy, and human validation.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AITest Design AITest Data GenerationDefect Analysis AITest Automation SupportQA Reporting & Workflow Automation

Who should attend

  • Software Test Engineers
  • QA Engineers
  • Quality Assurance Analysts
  • Manual Testers
  • Automation Test Engineers
  • Software Quality Engineers
  • Test Analysts
  • QA Executives
  • Application Testing Professionals
  • UAT Coordinators
  • Test Leads
  • Software Developers involved in Testing
  • Quality Engineering Professionals
  • Software Testing & QA Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software applications, testing, or software-development processes
  • Familiarity with requirements, test cases, defects, or testing cycles is helpful
  • Basic computer, spreadsheet, and document-handling skills
  • No AI or programming knowledge required
  • No previous AI training required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to software testing and quality assurance
  • Identifying AI applications across requirements review, test design, execution support, defect analysis, and reporting
  • Understanding AI assistance versus tester and QA professional judgement
  • Recognising limitations such as hallucinations, incomplete coverage, incorrect assumptions, and unsupported test conclusions
Practical activities
  • Mapping a typical Software Testing & QA workflow
  • Identifying repetitive and information-intensive testing activities suitable for AI assistance
  • Comparing a traditional testing task with an AI-assisted approach

Scenarios

User Story to Complete Test Pack

User Story → AI-Assisted Requirement Analysis → Test Scenarios → Test Cases → Test Data → Coverage Review → QA Validation

Participants use AI to convert a sample user story into structured test scenarios, detailed test cases, and representative test data while identifying gaps requiring clarification.

Test Execution to Release Quality Report

Test Results + Defects + Regression Status + Open Risks → AI Analysis → Defect Trends → Coverage Gaps → Release Concerns → QA Management Report

Participants use AI to analyse sample testing information, identify recurring quality concerns and unresolved risks, and prepare a management-ready QA and release-readiness summary.

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