Role-Based Programme
RB0915

Amazon Q Developer for Quality Management

Advanced AI-Assisted Testing, Code Quality & Continuous Improvement

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

Programme Objectives

  • Develop advanced proficiency in using Amazon Q Developer across software-quality planning, test generation, code review, defect investigation, remediation, and continuous-improvement workflows.
  • Apply context-aware and agentic AI workflows to analyse application code, generate and strengthen tests, investigate defects, and improve maintainability.
  • Use Amazon Q Developer code reviews to identify quality, security, secrets, and infrastructure-as-code issues and integrate findings into quality-governance processes.
  • Build repeatable AI-assisted workflows for regression testing, root-cause investigation, release readiness, technical documentation, modernisation, and quality reporting.
  • Establish responsible AI quality-engineering practices covering validation, traceability, permissions, security, human approval, and measurable quality gates.

Tools covered

Amazon Q DeveloperIDE ChatAgentic CodingContext-Aware ChatInline SuggestionsUnit Test GenerationCode ReviewSASTSecrets DetectionInfrastructure-as-Code ReviewCode ExplanationFixRefactor & OptimisationAWS Console TroubleshootingCode TransformationMCP Integration

Who should attend

  • Quality Managers – Software & Digital Products
  • Quality Assurance Managers
  • Quality Engineering Managers
  • Software Quality Engineers
  • QA Engineers
  • Test Engineers
  • Test Automation Engineers
  • QA & Test Leads
  • Application Quality Professionals
  • DevOps & DevSecOps Quality Professionals
  • Software Development Leads
  • Technical Quality Analysts
  • Release & Validation Professionals
  • Engineering Excellence Teams
  • Software Process Improvement Professionals

Prerequisites & Participant Readiness

  • Good understanding of software-development and testing lifecycles
  • Familiarity with functional testing, defects, regression testing, and acceptance criteria
  • Basic ability to read source code is recommended
  • Familiarity with an IDE such as Visual Studio Code, JetBrains, Eclipse, or Visual Studio is helpful
  • Basic understanding of Git and application-development workflows is recommended
  • Familiarity with AWS environments is helpful for operational-quality modules
  • No machine-learning or AI-development expertise required
  • Prior exposure to software-quality activities is strongly recommended

TOC Modules

Concepts
  • Understanding Amazon Q Developer within the software-development and quality lifecycle
  • Exploring IDE chat, context, inline suggestions, agentic coding, reviews, transformations, and troubleshooting
  • Mapping Requirements → Development → Testing → Review → Release → Operations
  • Identifying appropriate AI assistance versus mandatory human validation
Practical activities
  • Configuring Amazon Q Developer in a supported development environment
  • Exploring a sample application using Amazon Q
  • Asking quality-focused questions about application structure and behaviour
  • Building an initial AI-Assisted Quality Engineering workflow

Scenarios

Release Quality Assurance & Defect Prevention

New Application Release → Requirements Review → Code Context → Test Generation → Regression Analysis → Amazon Q Code Review → Security & Quality Findings → Remediation → Retest → Release Decision

Participants use Amazon Q Developer across an application-release cycle to strengthen test coverage, identify code and security issues, remediate validated findings, and create evidence for a human-controlled release-readiness decision.

Production Defect to Continuous Improvement

Production Failure → Error / Logs / Code Context → Amazon Q Investigation → Root-Cause Hypotheses → Evidence Validation → Code Fix → Automated Tests → Regression Validation → Preventive Action

Participants investigate a realistic production-quality incident using Amazon Q Developer, validate the root cause rather than accepting AI conclusions automatically, implement a controlled correction, strengthen regression tests, and convert lessons learned into preventive quality actions.

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