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SDET reviewing AI-assisted automation code against tests security checks and maintainability
SQA, Test Automation & SDET · Course #063

AI-Powered SDET

AI-Powered SDET is built around the decisions practitioners make when they need to integrate generative AI into SDET analysis, coding, debugging and maintenance workflows with evaluation, security and human-review controls. Learners move from ai augmentation across the sdet lifecycle into context engineering for repositories and automation frameworks, then apply requirement, risk and test-design assistance in realistic exercises. Participants use AI to explore code and requirements, draft automation, generate data, diagnose failures and refactor utilities, then test the output with static checks, execution evidence and review rubrics. Discussion and review activities highlight common failure modes, responsible tool use and the evidence needed to communicate confidence. They build an augmented engineering workflow where productivity claims are measured and generated code remains owned, understood and maintainable.

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Overview

AI-Powered SDET is built around the decisions practitioners make when they need to integrate generative AI into SDET analysis, coding, debugging and maintenance workflows with evaluation, security and human-review controls. Learners move from ai augmentation across the sdet lifecycle into context engineering for repositories and automation frameworks, then apply requirement, risk and test-design assistance in realistic exercises. Participants use AI to explore code and requirements, draft automation, generate data, diagnose failures and refactor utilities, then test the output with static checks, execution evidence and review rubrics. Discussion and review activities highlight common failure modes, responsible tool use and the evidence needed to communicate confidence. They build an augmented engineering workflow where productivity claims are measured and generated code remains owned, understood and maintainable.

Course Highlights

AI-assisted SDET workflow

Repository-aware analysis

Automation drafting and refactoring

Failure and log diagnosis

Generated-code evaluation

Modules & Curriculum

AI augmentation across the SDET lifecycle

Context engineering for repositories and automation frameworks

Requirement, risk and test-design assistance

Generating test code, data and utility functions

Refactoring and documentation with engineering constraints; failure diagnosis from logs, traces and diffs

Static analysis, execution and review of generated code

Security, secrets, licensing and privacy boundaries

Evaluation sets and productivity-quality measures; aI-assisted SDET workflow implementation

Learning Outcomes

  • Select SDET tasks suitable for AI assistance and define success measures.
  • Ground AI requests in repository, architecture and test context.
  • Review generated automation for correctness, security and maintainability.
  • Use AI hypotheses to accelerate—but not replace—failure diagnosis.
  • Create evaluation checks for repeated AI-assisted tasks.

Ready to Build Your Future-Ready Skills?

Augment SDET work with AI while preserving code ownership, evidence and engineering quality.

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