Role-Based Programme
RB0607

JetBrains AI Assistant for Quality Management

Strengthen Software Quality, Testing & Defect Analysis with AI

IMAGE REQUIRED
Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in using JetBrains AI Assistant across software quality analysis, testing, defect investigation, and release validation.
  • Use contextual AI capabilities to understand code behaviour, identify potential problems, and improve test coverage.
  • Apply AI-assisted unit-test generation, code self-review, defect analysis, and change inspection within structured QA workflows.
  • Improve collaboration between quality, development, product, release, and engineering teams through clearer technical evidence.
  • Establish governance practices for AI-generated findings, source-code access, review standards, sensitive information, and human validation.

Tools covered

JetBrains AI AssistantAI ChatCodebase ContextExplain CodeFind ProblemsSelf-Review with AIGenerate Unit TestsAI ActionsVCS AI AssistanceProject Rules`.aiignore`Coding Agents

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of software testing and quality-assurance practices
  • Familiarity with functional testing, defects, test cases, and release processes
  • Basic understanding of source code and application architecture
  • Familiarity with at least one programming language is recommended
  • Basic knowledge of Git or version-control concepts is helpful
  • Experience with test automation is beneficial
  • Access to a supported JetBrains IDE with AI Assistant is recommended

TOC Modules

Concepts
  • Understanding AI Assistant within JetBrains IDEs
  • Exploring AI Chat, contextual assistance, AI Actions, and project-aware workflows
  • Identifying quality-management applications across analysis, testing, review, and release
  • Differentiating AI-generated observations from confirmed defects
Practical activities
  • Exploring AI Assistant within a sample application
  • Asking contextual questions about application behaviour
  • Reviewing the source context used by AI Assistant
  • Building Quality Question → Code Context → AI Analysis → QA Validation workflow

Scenarios

Feature Build to Quality Gate

Feature Requirement → Code Understanding → Find Problems → Generate Unit Tests → Edge-Case Review → AI Self-Review → Regression Scope → QA Approval

Participants analyze a newly developed feature, identify potential quality concerns, improve its test coverage, and prepare it for formal quality validation.

Production Defect to Regression Prevention

Customer Defect → Logs & Reproduction Steps → Code Context → AI-Assisted Investigation → Root-Cause Validation → Fix Review → Regression Tests → Release Verification

Participants use technical evidence and AI assistance to investigate a defect, validate its cause with engineering, and strengthen tests to reduce recurrence.

## Current Capability Reference

JetBrains AI Assistant 2026.2 currently provides **Find Problems**, which analyzes selected code, identifies potential issues, and suggests possible fixes. This can support quality engineers during preliminary code-quality reviews, but identified problems still require technical validation.

AI Assistant can currently **generate unit tests using both selected code and surrounding code context**. Generated tests are presented as reviewable changes, and users can add additional scenario requirements before accepting them into the project.

JetBrains also provides **Self-Review with AI** for uncommitted or committed changes. Teams can provide a Markdown file containing project-specific code-review guidelines so AI Assistant evaluates changes against defined quality expectations.

Current version-control capabilities can **explain commits and generate pull/merge-request titles and descriptions**, supporting change-impact analysis and regression-test planning for QA teams.

For governance, JetBrains AI Assistant supports **Project Rules** for coding and quality conventions and `.aiignore` for restricting AI access to selected files and folders. Projects can also disable AI Assistant completely where source-code or information-security policies require it.

JetBrains AI Assistant also integrates multiple **coding agents** and supports reusable agent instructions such as `AGENTS.md`, allowing organizations to define testing expectations, development constraints, required checks, and definition-of-done criteria for agent-assisted engineering workflows.

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