Role-Based Programme
RB1515

AI-Powered Software Testing & Quality Assurance

Intelligent Test Design, Defect Analytics & QA Automation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across software testing, Quality Assurance, test planning, test design, execution, defect management, automation, and reporting.
  • Use AI to analyse requirements, user stories, acceptance criteria, application behaviour, test results, logs, defects, and release-quality information.
  • Build repeatable AI-assisted workflows for test-case generation, test-data preparation, regression planning, defect triage, root-cause support, and QA reporting.
  • Apply AI to identify coverage gaps, recurring defects, flaky tests, high-risk application areas, release-quality concerns, and automation opportunities.
  • Design responsible AI-enabled software QA workflows with appropriate controls for code security, data privacy, traceability, test validity, release governance, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchCoding AssistantsTest Management ToolsAutomated Testing ToolsAPI Testing ToolsLog & Defect AnalyticsSpreadsheet & Data AnalysisReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Software Test Engineers
  • Quality Assurance Engineers
  • QA Analysts
  • Software QA Managers
  • Test Automation Engineers
  • Manual Test Engineers
  • Senior Test Engineers
  • Test Leads
  • QA Leads
  • Software Quality Engineers
  • Application Testing Professionals
  • API Testing Professionals
  • Performance Testing Professionals
  • Quality Engineering Professionals
  • Software Testing & QA Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of software testing, Quality Assurance, application development, or software delivery
  • Familiarity with requirements, test cases, defects, test execution, releases, and software development lifecycles
  • Basic proficiency with spreadsheets, technical documentation, test-management tools, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, coding assistants, and test automation
  • Mapping AI opportunities across the software testing and QA lifecycle
  • Understanding AI assistance versus accountable QA and release decisions
  • Recognising risks from hallucinated tests, incomplete coverage, insecure code, and weak validation
Practical activities
  • Mapping an existing software testing lifecycle
  • Comparing manual and AI-assisted QA activities
  • Creating a Software Testing AI opportunity matrix

Scenarios

Requirement to Release Quality Decision

Requirements → Testability Review → Risk-Based Test Plan → Test Cases → Test Data → Execution → Defect Analysis → Regression → Quality Dashboard → Release Review

Participants use AI-assisted techniques to test a simulated software release, identify requirement and coverage gaps, analyse defects, optimise regression testing, and prepare a release-quality assessment.

QA Data to Intelligent Testing Workflow

Requirements + Test Results + Defects + Logs + Regression History + Release Changes → AI Analysis → Coverage Gaps → Risk-Based Test Selection → Automated Execution & Triage → QA Dashboard → Team Review

Participants design an AI-enabled Software Testing & Quality Assurance workflow that consolidates quality signals, improves test prioritisation, accelerates defect analysis, automates appropriate QA activities, and strengthens release visibility while retaining final quality and release decisions with authorised professionals.

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