Role-Based Programme
RB0858

Codex for Quality Management

Advanced AI-Assisted Testing, Code Review & Quality Engineering

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

Programme Objectives

  • Develop advanced expertise in using Codex for software-quality analysis, test engineering, defect investigation, regression validation, and release assurance.
  • Architect reusable agentic QA workflows for requirements analysis, test generation, code review, automated validation, and defect remediation.
  • Apply parallel Codex agents to independently analyse code, testing, defects, security, and release risks.
  • Integrate Codex into Git, pull-request, test, CI/CD, and quality-governance workflows while maintaining traceability and human control.
  • Establish enterprise-quality standards for AI-generated tests, fixes, reviews, security findings, and release evidence.

Tools covered

OpenAI CodexCodex AppCodex CLIIDE IntegrationCloud TasksParallel AgentsGit WorktreesCode ReviewSkillsAutomationsCodex Security

Who should attend

  • Quality Assurance Managers
  • Software Quality Engineers
  • QA Engineers
  • Test Automation Engineers
  • Software Test Engineers
  • Test Leads & QA Leads
  • Quality Engineering Professionals
  • Software Validation Professionals
  • DevOps Quality Engineers
  • Application Quality Professionals
  • Release & Validation Managers
  • Software Quality Architects
  • Engineering Quality Leaders
  • SDET Professionals

Prerequisites & Participant Readiness

  • Working knowledge of software testing and quality-assurance processes
  • Familiarity with test cases, defects, regression testing, and release validation
  • Basic ability to read source code and understand software architecture
  • Familiarity with Git, repositories, pull requests, or CI/CD is recommended
  • Basic scripting or test-automation experience is beneficial
  • No previous Codex course is required

TOC Modules

Concepts
  • Understanding Codex across application, IDE, terminal, and cloud-based development workflows
  • Understanding agentic software engineering and its impact on modern QA
  • Mapping Codex capabilities to testing, review, validation, and release assurance
  • Understanding human accountability for AI-generated technical outputs
Practical activities
  • Exploring a controlled application using Codex
  • Running an initial code, test, and quality-analysis task
  • Establishing a baseline AI-assisted QA workflow

Scenarios

Enterprise Release Quality Assurance

Requirements → Code Changes → Parallel Codex Reviews → Test Generation → Automated Execution → Defect Investigation → Security Validation → Regression → Quality Gate → Release Recommendation

Participants orchestrate Codex across an enterprise application release to independently review code, generate and execute tests, investigate defects, validate remediation, and create traceable evidence for a controlled release decision.

Continuous AI-Assisted Quality Engineering

Repository Change → Automated Quality Workflow → Impact Analysis → Code Review → Targeted Regression → Security Check → Findings → Human Review → Quality Dashboard / Report

Participants design a reusable quality-engineering workflow in which Codex assists with ongoing change analysis, regression selection, review, testing, and escalation while preserving human approval for significant quality and release decisions.

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