Role-Based Programme
RB0617

Firebase Studio for Quality Management

AI-Assisted Software Testing, Validation & Quality Engineering

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational capability to use Firebase Studio for application-quality review, functional validation, defect investigation, and test support.
  • Convert requirements and acceptance criteria into structured test scenarios for AI-generated and conventionally developed applications.
  • Use application previews, Gemini assistance, Code View, and Firebase emulators to identify and investigate quality issues.
  • Validate authentication, application data, error handling, edge cases, and AI-enabled application behaviour.
  • Apply human review, security awareness, traceability, regression thinking, and quality gates before application release.

Tools covered

Firebase StudioGemini in Firebase StudioApp Prototyping AgentCode ViewFirebase Local Emulator SuiteCloud FirestoreFirebase AuthenticationGenkit Developer UIDebugging & Preview Tools

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of software quality assurance and testing
  • Familiarity with requirements, test cases, defects, and acceptance criteria
  • Basic awareness of web applications and application workflows
  • No advanced programming expertise required
  • No previous Firebase Studio experience required
  • Hands-on practice requires an existing or instructor-provided Firebase Studio workspace because creation of new workspaces is no longer supported

TOC Modules

Concepts
  • Understanding Firebase Studio, Gemini assistance, Prototyper, Code View, and application previews
  • Mapping quality activities across Requirement → Build → Test → Defect → Retest → Release
  • Understanding AI-generated applications and their additional validation requirements
  • Differentiating product demonstration, functional testing, and production readiness
Practical activities
  • Exploring an existing Firebase Studio application
  • Reviewing its main workflows from a QA perspective
  • Creating an initial application-quality checklist

Scenarios

AI-Generated Application to Quality Sign-Off

Business Requirement → Acceptance Criteria → Firebase Studio Prototype → Functional Tests → Defects → Gemini-Assisted Investigation → Retest → Regression → QA Recommendation

Participants evaluate an AI-generated application against documented requirements and produce an evidence-based quality recommendation before stakeholder acceptance.

Secure User Workflow to Release Validation

User Requirement → Authentication → Firestore Data → Emulator Tests → Positive & Negative Scenarios → Security-Rule Review → Defect Resolution → Release Quality Gate

Participants validate a user-facing workflow involving identity and application data, identifying functional and access-control issues before release.

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