AI in Software Testing Courseware (AIST)

Module 1: Foundations of AI in Testing

• Introduction to AI in Software Testing

• Benefits and use cases of AI for QA

• Overview of AI tools: GitHub Copilot, ChatGPT, Applitools, Launchable, and more

• Understanding zero-shot and few-shot prompting

Module 2: AI-Driven Test Case Generation

• Writing effective prompts for test creation

• Generating unit and edge case tests using LLMs

• Prompt patterns and strategies for maximizing test relevance

• Evaluating and refining AI-generated test cases

Module 3: AI-Assisted Code Coverage and Refactoring

• Measuring code coverage (line, branch, function)

• Using AI to detect gaps in coverage

• Refactoring verbose or redundant tests

• Mutation testing overview

Module 4: Testing Legacy Code with AI

• Understanding undocumented code with LLMs

• Generating regression tests for legacy behavior

• Using AI to reverse-engineer and protect critical functionality

Module 5: Exploratory and Edge Case Testing

• Defining exploratory testing and its value

• Generating edge cases with AI (fuzzing, boundary tests)

• Handling complex or malformed input scenarios

Module 6: Generating Synthetic Test Data

• Creating structured and unstructured data using AI

• Valid vs. invalid input generation

• Risks: hallucinations, unrealistic data, format constraints

Module 7: Detecting and Fixing Test Smells

• Common anti-patterns in test code

• Using AI to clean up, rename, and restructure tests

• Improving maintainability and test intent clarity

Module 8: Testing AI and Machine Learning Systems

• Unique challenges in testing non-deterministic output

• Output validation via heuristics, type checks, and human-in-the-loop

• Designing robust, behavior-focused test cases

Module 9: Test Maintenance and Flaky Tests

• Identifying causes of flaky tests (async, timing, randomness)

• Diagnosing issues with AI analysis of logs and failures

• Stabilizing tests with mocks, retries, and dependency control

Module 10: CI/CD Integration

• Incorporating AI-generated tests into CI workflows

• Using GitHub Actions for automated test runs

• Reviewing and tagging AI-generated content

• Managing regression lifecycles and metrics

Module 11: Documentation and Reporting with AI

• Auto-generating test documentation and summaries

• Writing JSDoc-style comments and QA reports

• Using LLMs for stakeholder-friendly communication

Module 12: Limitations, Ethics, and Trust

• Understanding hallucinations, overconfidence, and logic gaps

• Mitigating risk with prompt design and human oversight

• Intellectual property and authorship concerns in AI-generated code

Module 13: Capstone Project

• Apply AI techniques to a full-stack JavaScript application

• Generate, refactor, document, and integrate tests

• Demonstrate your complete AI-enhanced testing workflow in CI/CD