Role-Based Programme
RB0608

JetBrains AI Assistant for Quality Management

Master AI-Assisted Testing, Code Review & Release Assurance

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

Programme Objectives

  • Develop advanced expertise in using JetBrains AI Assistant across software testing, defect prevention, code-quality analysis, debugging, and release assurance.
  • Architect AI-assisted quality workflows covering requirements, test design, unit testing, code review, regression, root-cause analysis, and technical documentation.
  • Apply project rules, reusable prompts, coding agents, MCP integrations, and quality standards to improve consistency across development teams.
  • Integrate AI-assisted review and testing into Git and continuous quality workflows while maintaining human verification and traceability.
  • Establish governed Feature → Build → Test → Review → Fix → Regression → Release workflows suitable for enterprise software-quality environments.

Tools covered

JetBrains AI AssistantAI ChatInline Code CompletionNext Edit SuggestionsAI ActionsGenerate Unit TestsSelf-Review with AIFind ProblemsExplain CodeAI RefactoringAI DiffPrompt LibraryProject RulesCoding AgentsAgent InstructionsModel Context Protocol (MCP)Agent Client Protocol (ACP)VCS IntegrationLocal & Third-Party Models

Who should attend

  • Quality Managers
  • Quality Engineering Managers
  • QA Leads
  • Software Quality Engineers
  • Software Test Engineers
  • Test Automation Engineers
  • SDET Professionals
  • Quality Architects
  • Code Quality Professionals
  • Application Quality Engineers
  • Release Quality Managers
  • Software Development Leads
  • DevOps & Quality Engineering Professionals
  • Technical Quality Team Leads

Prerequisites & Participant Readiness

  • Good understanding of software testing and quality-engineering concepts
  • Familiarity with unit, integration, regression, functional, and acceptance testing
  • Ability to read or work with application source code in at least one programming language
  • Familiarity with Git, commits, branches, pull requests, and code-review processes
  • Basic understanding of IDE-based software-development workflows
  • Experience with test automation frameworks is beneficial
  • Access to a supported JetBrains IDE and enabled AI Assistant capability
  • No previous JetBrains AI Assistant course completion required

TOC Modules

Concepts
  • Understanding AI Assistant within JetBrains IDE development and quality workflows
  • Understanding AI Chat, AI Actions, code context, model selection, and coding agents
  • Understanding Chat mode versus agent-driven multi-step execution
  • Mapping AI capabilities across prevention, detection, diagnosis, correction, and verification
  • Understanding model limitations and the requirement for human validation
Practical activities
  • Exploring AI Assistant through a quality-engineering project
  • Attaching relevant code, test files, symbols, and project context
  • Mapping Development Activity → Quality Risk → AI Capability → Verification Control

Scenarios

Feature Development to Governed Release Quality Gate

Feature Requirement → Risk Analysis → Code Context → AI-Assisted Test Generation → Test Execution → Find Problems → Self-Review with AI → Defect Correction → Regression Testing → Peer Review → Release Recommendation

Participants establish an AI-assisted quality gate around a realistic software change, combining test generation, code-quality review, defect prevention, regression assurance, and human approval before release.

Production Defect to Preventive Quality Improvement

Production Failure → Error / Code Evidence → AI-Assisted Root-Cause Analysis → Coding Agent Investigation → Controlled Fix → New Regression Tests → Project Rule Update → Self-Review → VCS Traceability → Preventive Quality Action

Participants investigate a production-style defect, validate its root cause, correct the issue, create regression protection, and strengthen project-level quality rules to reduce recurrence.

## Current Capability Reference

JetBrains' current **AI Assistant 2026.2** supports code generation and updates, inline code completion, code explanation, refactoring, potential-problem identification, documentation generation, unit-test generation, commit-message generation, and pull-request/change summaries.

The current **Generate Unit Tests** workflow analyses both selected code and surrounding context, presents generated tests in **AI Diff**, allows additional requirements to be specified, and can add tests to existing test files or create new test modules where required.

**Self-Review with AI** can review uncommitted changes as well as existing commits before release, and organisations can provide a Markdown-based review-guideline file so AI Assistant applies project-specific code-review requirements.

Current AI Assistant also supports **coding agents** capable of multi-step project work such as editing files, executing commands and tests, and using external tools. Its current feature set includes **MCP, ACP, Project Rules, agent instruction files, Prompt Library, and configurable local or third-party models**.

For enterprise governance, AI Assistant can be disabled at project level and `.aiignore` can restrict access to selected files or folders, providing additional controls for projects containing sensitive or restricted source material.

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