Role-Based Programme
RB0606

JetBrains AI Assistant for Quality Management

AI-Assisted Testing, Code Review & Software Quality Assurance

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational capability in using JetBrains AI Assistant to support software testing, defect analysis, code-quality review, and release validation.
  • Translate requirements and acceptance criteria into structured test scenarios, edge cases, and quality checks.
  • Use project-aware AI assistance to understand code, investigate defects, generate tests, and identify potential quality risks.
  • Apply AI-assisted workflows to regression testing, code review, documentation, and developer–QA collaboration.
  • Maintain human validation, security, confidentiality, traceability, and quality accountability when using AI-generated recommendations.

Tools covered

JetBrains AI AssistantAI ChatProject ContextCode ExplanationTest GenerationCode CompletionRefactoring AssistanceError ExplanationCommit ContextDocumentation AssistanceJunie Coding AgentProject Instructions

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of software testing and quality-assurance concepts
  • Familiarity with requirements, acceptance criteria, test cases, and defect reporting
  • Basic awareness of source code and application-development workflows is helpful
  • Some exposure to an IDE or software-development environment is recommended
  • No advanced programming expertise required
  • No previous JetBrains AI Assistant experience required

TOC Modules

Concepts
  • Understanding JetBrains AI Assistant within the software-development and quality lifecycle
  • Understanding AI Chat, project context, code explanation, and AI-assisted development workflows
  • Identifying appropriate applications across testing, code review, debugging, and release assurance
  • Recognising where QA judgement and engineering validation remain essential
Practical activities
  • Exploring AI Assistant within a sample application project
  • Asking project-aware questions about application structure and functionality
  • Creating an initial quality-risk map for selected application components

Scenarios

Feature Requirement to Release Validation

Approved Requirement → Testability Review → AI-Assisted Test Design → Code Context Review → Test Execution → Defect Identification → Fix Review → Regression Testing → Release Recommendation

Participants use JetBrains AI Assistant throughout a controlled QA workflow while retaining human ownership of test adequacy and release-quality decisions.

Production Defect to Quality Improvement

Production Issue → Logs & Error Evidence → AI-Assisted Investigation → Suspected Code Area → Engineering Fix → Targeted Tests → Regression Pack → Root-Cause Learning

Participants analyse a realistic application defect, strengthen test coverage around the failure, and convert the lesson into reusable quality checks for future releases.

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