Role-Based Programme
RB0856

Codex for Quality Management

AI-Assisted Testing, Code Review & Software Quality Assurance

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational proficiency in using Codex for software testing, code-quality review, defect investigation, and validation activities.
  • Apply AI-assisted techniques to analyse requirements, create test scenarios, review implementation changes, and identify potential software defects.
  • Use repository-aware Codex workflows to understand code behaviour, dependencies, test coverage, and quality risks.
  • Create repeatable workflows for defect analysis, regression testing, code review, and release-readiness assessment.
  • Apply appropriate human review, traceability, security, and validation controls to AI-assisted quality workflows.

Tools covered

OpenAI CodexCodex in ChatGPTCodex CLIRepository ContextCode ReviewTest GenerationDebuggingAgentic CodingParallel AgentsSkills

Who should attend

  • Quality Assurance Professionals
  • Software Quality Engineers
  • QA Engineers
  • Software Test Engineers
  • Test Analysts
  • Automation Test Engineers
  • Software Development Engineers in Test
  • Application Quality Professionals
  • Quality Engineering Professionals
  • QA Team Leads
  • Technical Quality Leads
  • Software Quality Managers

Prerequisites & Participant Readiness

  • Basic understanding of software testing or quality-assurance activities
  • Familiarity with requirements, test cases, defects, or software-development workflows
  • Basic programming or scripting knowledge is helpful
  • Familiarity with Git or source-code repositories is beneficial
  • No advanced development expertise required
  • No previous Codex experience required

TOC Modules

Concepts
  • Understanding Codex as an AI coding agent for software engineering and quality workflows
  • Understanding repository context, code changes, testing, and agent-based task execution
  • Identifying applications across testing, defect analysis, code review, and release validation
  • Understanding AI-assisted review as support rather than a replacement for quality ownership
Practical activities
  • Exploring a sample repository using Codex
  • Asking Codex to explain application functionality and existing test coverage
  • Creating an initial quality-review checklist

Scenarios

Feature Requirement to Release Validation

Feature Requirement → Acceptance Criteria → Codex Test Generation → Code Review → Defect Identification → Fix → Regression Testing → Release Recommendation

Participants use Codex to support a complete software-quality workflow from requirement analysis through testing, defect correction, and release-readiness review.

Production Defect to Quality Improvement

Production Issue → Repository & Test Analysis → Root-Cause Hypothesis → Corrective Change → Automated Tests → Regression Review → Human Validation → QA Closure

Participants investigate a simulated production issue, review Codex-generated findings and corrective changes, strengthen regression coverage, and prepare structured quality-closure documentation.

*Codex currently supports end-to-end engineering tasks including code changes, testing, code review, parallel agent workflows, reusable Skills, and repository-aware development. OpenAI recommends retaining human ownership of final review and release decisions.*

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