Role-Based Programme
RB1514

AI-Powered Software Testing & Quality Assurance

Smarter Test Design, Defect Analysis & Quality Engineering

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Software Testing and Quality Assurance activities including requirement analysis, test planning, test design, execution support, defect management, and reporting.
  • Use AI-assisted techniques to generate and improve test scenarios, test cases, acceptance criteria, test data ideas, and traceability documentation.
  • Develop structured workflows for functional testing, regression testing, defect analysis, root-cause support, release-quality assessment, and QA reporting.
  • Analyse testing and defect data to identify coverage gaps, recurring failures, quality risks, bottlenecks, and improvement opportunities.
  • Apply responsible AI practices covering confidential software information, test-data privacy, output validation, traceability, security, and human oversight.

Tools covered

Generative AI AssistantsTest Case Generation SupportRequirements AnalysisDefect AnalysisTest Data SupportDocument IntelligenceSpreadsheet AnalysisRegression Testing SupportQA ReportingWorkflow Automation

Who should attend

  • Software Test Engineers
  • Quality Assurance Engineers
  • QA Analysts
  • Software Quality Engineers
  • Test Analysts
  • Functional Testers
  • Regression Testing Professionals
  • Test Leads
  • QA Leads
  • Quality Engineering Professionals
  • Application Support Test Professionals
  • Business Acceptance Testing Professionals
  • Software Delivery Professionals
  • Information Technology Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of software testing, QA, application development, or software-delivery activities
  • Familiarity with requirements, test cases, defects, regression testing, or release cycles is helpful
  • Basic spreadsheet and documentation skills
  • Basic awareness of Generative AI is helpful
  • No advanced programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, automation, and their role in Software Testing
  • Identifying AI applications across test planning, test design, defect analysis, and reporting
  • Understanding AI assistance versus tester and QA professional judgement
  • Recognising risks related to inaccurate test logic, sensitive code, data privacy, and unsupported conclusions
Practical activities
  • Mapping the software-testing lifecycle to AI-assisted activities
  • Identifying repetitive QA tasks suitable for AI support
  • Comparing traditional and AI-assisted testing workflows

Scenarios

New Feature to Release-Readiness Assessment

Business Requirement → AI-Assisted Testability Review → Test Scenarios → Test Cases → Execution Results → Defects → Regression Coverage → Release-Readiness Summary

Participants analyse a simulated software feature, design comprehensive test coverage, review test execution and defects, and prepare an evidence-based release-readiness assessment.

Defect & Test Data to Quality Improvement Plan

Test Results + Defect Records + Regression Data + Requirement Coverage → AI Analysis → Recurring Failures → Coverage Gaps → Quality Risks → Improvement Actions → QA Management Report

Participants consolidate software-testing information, identify recurring defects and weak test coverage, and prepare a management-ready QA improvement plan with actions, owners, and priorities.

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