Role-Based Programme
RB0605

JetBrains AI Assistant for Quality Management

AI-Assisted Testing, Defect Analysis & Software Quality Validation

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how JetBrains AI Assistant can support software testing, code-quality review, defect investigation, and validation activities.
  • Explore AI-assisted unit-test generation, code explanation, problem detection, and runtime-error analysis within JetBrains IDEs.
  • Experience converting requirements and expected behaviour into meaningful test scenarios and AI-assisted test-generation instructions.
  • Discover practical workflows for defect investigation, regression thinking, and developer–QA collaboration.
  • Recognise the importance of human validation, security, test coverage, and engineering review when using AI-generated tests or fixes.

Tools covered

JetBrains AI AssistantAI ChatGenerate Unit TestsFind ProblemsExplain CodeExplain Runtime ErrorsProject ContextAI DiffAI Actions

Who should attend

  • Quality Assurance Managers
  • Software Quality Managers
  • QA Engineers
  • Quality Engineers
  • Software Test Engineers
  • Test Analysts
  • Test Automation Professionals
  • Functional Testing Professionals
  • Application Quality Analysts
  • Release Quality Professionals
  • QA Leads
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software testing and quality-assurance practices
  • Familiarity with requirements, test cases, expected results, and software defects
  • Basic awareness of source code and development workflows is helpful
  • Advanced programming expertise is not required
  • No previous JetBrains AI Assistant experience required
  • Hands-on activities require a supported JetBrains IDE with AI Assistant enabled

TOC Modules

Concepts
  • Understanding JetBrains AI Assistant within development and QA workflows
  • Exploring AI Chat, project context, Explain Code, Find Problems, and test-generation capabilities
  • Mapping AI assistance across Understand → Test → Detect → Investigate → Validate
  • Understanding AI assistance as supplementary quality support rather than automated quality approval
Practical activities
  • Exploring AI Assistant within a sample application project
  • Asking AI Chat to explain the behaviour of a selected feature
  • Identifying potential areas requiring quality validation

Scenarios

New Feature to AI-Assisted Quality Validation

Feature Requirement → Code Context → AI-Generated Unit Tests → Test Review → Execute → Defect Identification → Fix Validation → Regression Check → QA Recommendation

Participants use JetBrains AI Assistant to support test creation and feature validation while maintaining human ownership of coverage, expected behaviour, and quality approval.

Runtime Failure to Verified Resolution

Runtime Error → Error Context → AI Explanation → Suspected Cause → Developer Review → Proposed Fix → Retest → Regression Validation → Defect Closure

Participants investigate a realistic application error using AI-assisted explanations and recommendations, then validate the resolution through structured testing before closure.

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