Role-Based Programme
RB0321

GitHub Copilot for Quality Management

Build AI-Assisted Testing, Code Review & Release Quality Workflows

IMAGE REQUIRED
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational capability in using GitHub Copilot across software testing, defect investigation, code review, and release-quality activities.
  • Translate requirements and acceptance criteria into structured test scenarios, validation conditions, and quality checks.
  • Use Copilot to understand code changes, investigate defects, generate tests, and strengthen regression coverage.
  • Apply Copilot Code Review and Pull Request workflows to identify potential defects and support release readiness.
  • Maintain human QA ownership, traceability, security awareness, and independent validation of AI-generated findings and fixes.

Tools covered

GitHub CopilotCopilot ChatGitHub Copilot Code ReviewCopilot Cloud AgentGitHub IssuesPull RequestsCustom InstructionsAGENTS.mdGit-Based Quality Workflows

Who should attend

  • Quality Assurance Managers
  • Software Quality Engineers
  • QA Engineers
  • Test Engineers
  • Automation Test Engineers
  • SDET Professionals
  • Quality Analysts
  • Application Quality Professionals
  • Software Test Leads
  • Release Quality Professionals
  • Quality Engineering Professionals
  • Quality Engineering Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software testing and quality-assurance processes
  • Familiarity with requirements, acceptance criteria, test cases, and defect management
  • Basic understanding of source code is helpful
  • Familiarity with GitHub Issues and Pull Requests is beneficial
  • General awareness of generative AI is helpful
  • No previous GitHub Copilot course completion required

TOC Modules

Concepts
  • Understanding GitHub Copilot across coding, testing, Pull Requests, reviews, and agent-assisted development
  • Mapping quality activities across Requirement → Build → Test → Review → Release
  • Understanding the role of AI assistance within modern quality-engineering workflows
  • Differentiating AI-generated observations from verified software defects
Practical activities
  • Exploring a representative repository, Issue, code change, and Pull Request with Copilot
  • Mapping Quality Activity → Copilot Capability → Human Validation → Quality Outcome

Scenarios

Feature Change to Quality-Validated Release

Business Requirement → Acceptance Criteria → GitHub Issue → Copilot-Assisted Test Design → Code Change → Pull Request → Copilot Code Review → Defect Correction → Regression Testing → QA Validation

Participants validate a representative feature from requirement through release readiness while ensuring AI-generated tests, findings, and fixes are independently reviewed against defined quality criteria.

Production Defect to Preventive Quality Improvement

Production Defect → Evidence & Code Context → Copilot-Assisted Investigation → Root-Cause Validation → Fix → Targeted Tests → Pull Request Review → Regression Coverage → Quality Instruction Update → Release Decision

Participants investigate and resolve a recurring software defect and convert lessons learned into reusable quality instructions and regression controls to reduce the likelihood of similar failures.

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