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Advanced · Agentic AI & Automation

A2Agentic QA Systems and Automation

Reinvent software quality with AI: generated tests, self-healing suites, and agentic CI/CD pipelines.

Who it's for

Technical professionals with a solid foundation in QA, basic familiarity with large language models (LLMs), and basic experience with Robotic Process Automation (RPA).

You'll walk away with

An agentic QA proof of concept

Apply generative AI to automate and enhance software quality assurance, from AI-driven test generation to self-healing CI/CD pipelines.

Format

Lectures + hands-on workshops

Assessment

Pre- and post-tests (30 multiple-choice questions); hands-on workshops aligned to each learning outcome

Tools & platforms

PyTestFakerPandasRequestsPlaywrightQodo CoverQodo PR AgentFlowisen8n

Programme Curriculum

What you'll be able to do

  • Explain current academic and industry directions in GenAI-driven software quality assurance, including the capabilities and limitations of existing tools
  • Apply LLM techniques to generate, translate, and expand automated test suites from user stories and source code
  • Design semi-autonomous QA pipelines that combine AI-generated tests with human validation to improve coverage and reliability
  • Build and evaluate a proof-of-concept self-healing QA system with appropriate human oversight and governance

Your learning journey

  1. Module 1

    AI-Driven Test Generation and Modern QA Perspectives

    • Generative AI in Software QAGenerative AI for software quality assurance; LLM-generated tests; human-vs-AI test generation; PyTest
    • Prompt Engineering for Test DesignStructured prompting; iterative prompt refinement; AI-assisted test generation; output evaluation
    • Synthetic Test Data GenerationSynthetic data generation; Faker; Pandas; LLM-assisted dataset generation; data validity and realism
    • Test Coverage & Edge-Case AnalysisTest coverage analysis; edge-case identification; coverage gaps; limitations of LLM-generated tests
    • Reflection & DeliverableAI capability and limitation review; AI-generated test suite; synthetic dataset; reflection notes

  2. Module 2

    Translating and Executing AI-Generated Tests

    • Test Architecture FundamentalsTest fixtures; mocks; parameterization; test environment context
    • AI-Generated Test ExecutionLLM-generated PyTest execution; syntax validation; logic validation; test debugging and correction
    • API Test AutomationAPI testing; Requests; PyTest; AI-generated payload validation
    • UI Test AutomationUI automation; Playwright; timing issues; test flakiness
    • Reflection & DeliverableExecutable and maintainable AI-assisted tests; runnable multi-layer automation suite

  3. Module 3

    Coverage Expansion and Self-Healing Experimentation

    • Self-Healing Test AutomationDetect–reason–repair–re-run loop; self-healing test concepts
    • AI-Assisted Coverage AugmentationQodo Cover; automated test expansion; coverage delta analysis
    • LLM-Assisted Self-RepairFailing test analysis; Flowise; LLM-generated code patches; review and re-execution
    • Human-in-the-Loop GovernanceHuman-in-the-loop (HITL); approval policies; governance for AI-generated code changes
    • Reflection & DeliverableSelf-healing reliability assessment; safe automation practices; prototype self-repair workflow; coverage report

  4. Module 4

    Agentic QA Pipeline in CI/CD

    • Agentic QA & CI/CDAgentic QA; AI agents in Continuous Integration / Continuous Delivery (CI/CD); automated QA workflows
    • AI-Assisted Pull Request ReviewQodo PR Agent; automated test review; missing-test detection
    • End-to-End Agentic QA PipelineQodo Cover; Qodo PR Agent; n8n; detect–reason–fix–retest workflow
    • Observability & ReportingWorkflow observability; logging; automated summaries; notifications; n8n; Flowise
    • Showcase & DeliverableQA automation boundaries; proof-of-concept CI/CD QA pipeline; supervised self-healing

Remarks

  • Prerequisite: Completion of a pre-test before the first session