AI in Software Testing

Course Duration

1 Day

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

No prerequisites required.

Course Description

AI is beginning to reshape how testing is planned, written, and maintained, and this course helps you build the skills to apply it in ways that actually make your work easier. Whether you are creating test cases, choosing what to run, or reviewing failures, you will learn how to use AI tools to speed things up, reduce repetitive effort, and improve coverage. You will work hands-on with user-friendly AI tools generate test data, build tests from user stories, and support smarter decisions about what to test and when. You will practice spotting flaky or redundant tests, creating self-healing flows, and using AI to explain what went wrong in a failing run. You will also explore how AI can predict risk based on commit history or past bugs, helping you focus on the areas that matter most. The course will show you how to plug AI into common testing workflows, including CI/CD tools like GitHub Actions, and how to write prompts that give you useful, accurate results. You will get examples, use cases, and guided labs that you can use right away in your own projects. This expert-led, one-day course is designed for software testers who are new to AI but already familiar with core testing practices. It is about 50 percent hands-on, with labs built around common tasks that testers perform every day. Whether you are working in a manual, automated, or hybrid role, this course will help you start using AI in ways that are practical, helpful, and easy to build on.

Learning Objectives

• Use cutting-edge AI tools to generate realistic and varied test data tailored to your testing goals
• Select and prioritize test cases based on code changes, historical results, and risk indicators
• Generate UI, API, and functional test cases using plain-language prompts and AI coding tools
• Identify flaky or redundant tests and improve stability with self-healing and AI analytics
• Use AI to summarize test failures, detect patterns, and support faster triage
• Integrate AI-generated tests and insights into existing CI/CD workflows for real-time value

Course Outline

1. Introduction to AI in Software Testing

• What AI means in software testing
• Traditional vs AI-augmented workflows
• Key benefits: speed, coverage, accuracy
• AI in unit, integration, UI, and end-to-end testing
• Tool types for different user levels

2. Generating Test Data with AI

• Why high-quality test data matters
• Structured data with Mockaroo and Faker
• Using ChatGPT for edge-case inputs
• Choosing the right tool for your needs
• Keeping data anonymous yet realistic

3. Selecting Test Cases with AI

• Prioritize tests based on change history
• Remove redundant or low-value tests
• Select minimal test sets with high impact
• Generate tests from requirements
• Visualize impact with dashboards

4. AI-Enhanced Test Generation

• Use AI for UI, API, and functional test cases
• Convert user stories into test scripts
• Improve outputs with prompt tuning
• Explore Copilot, Testim, and Codeium
• Integrate generated tests in IDEs

5. Smart Test Execution and Maintenance

• Detect and debug flaky tests
• Find test bottlenecks with analytics
• Use self-healing selectors
• Perform visual regression testing
• Track evolving test failures

6. Defect Prediction using AI

• Forecast risk using test and code history
• Correlate bugs with churn and complexity
• Analyze commits using NLP
• Visualize risk zones with heatmaps
• Use insights in planning

7. Integrating AI into the Testing Workflow

• Identify AI entry points in the workflow
• Generate summaries and insights with ChatGPT
• Add AI to CI/CD (e.g., GitHub Actions)
• Use LLMs to triage test failures
• Standardize prompt best practices