Learn Generative AI in Software Testing from Udemy

This AI Testing course is designed to be your ideal companion, teaching prompt engineering skills that help you ask the right questions in the right way, enabling you to generate answers swiftly for your everyday testing needs. We will also explore AI-powered testing tools and delve into how QA Automation is evolving with AI intelligence.

The course content is divided into three learning phases:

Phase 1:

Creating test plans and requirements through AI.
Generating unit, integration, and functional test cases with AI.
Producing test data relevant to different tests.
Providing suggestions on distributing tests throughout the testing lifecycle.
Developing automation scripts for test cases.
Crafting custom utility code methods to automate functionalities.
Configuring framework-related files using AI.
Creating Cucumber feature files and step definitions with real code via AI.
Generating UI tests using libraries like Selenium, Cypress, and Playwright.

Phase 2:

An introduction to AI-powered testing tools.
Achieving codeless automation using AI QA tools.
Generating test automation code based on business analyst requirements within the tool.
Understanding the self-healing capabilities of AI tools to ensure test stability.
Learning about intelligent reporting and defect management with AI-powered QA tools.
Conducting an end-to-end demo on writing complex tests in plain English.

Phase 3:

Parsing complex JSON responses with simple AI prompts.
Generating JSON paths using plain English.
Creating POJO classes for complex JSON files with AI prompts.
Developing Rest Assured automation tests using contract documentation as input.
Generating custom utility code methods on-the-fly to validate API responses.
Producing AI tests using libraries like Rest Assured, Cypress, and Playwright.
Formulating SQL queries from complex database tables using simple AI prompts.

We utilize Google GEMINI (AI) to demonstrate topics in Phases 1 and 3.

What's inside

Learning objectives

Learn prompting skills to generate automation code in any language/tools (selenium,cypress,playwright) using ai
Understand how to optimize the code into framework standards with simple prompting to ai
Learn generating test artifacts in fly such as testplan, testcases, testdata, bug templates for given business requirements

Get overview of ai powered testing tools in current market and their capabilities for revolutinizing test automtion
Learn generating api automation tests to framework level & sql queries with simple prompting to ai

Learn prompting skills to generate automation code in any language/tools (selenium,cypress,playwright) using ai
Understand how to optimize the code into framework standards with simple prompting to ai
Learn generating test artifacts in fly such as testplan, testcases, testdata, bug templates for given business requirements
Get overview of ai powered testing tools in current market and their capabilities for revolutinizing test automtion
Learn generating api automation tests to framework level & sql queries with simple prompting to ai

Syllabus

Introduction to AI Testing terminologies

Introduction to AI Terminologies for Testing

3 phases of Course Curriculum overview

Generate Test Plan, Test Cases, Test Strategy & Test Data using AI

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches prompt engineering skills, enabling learners to effectively use AI to generate solutions for everyday testing needs, which is highly relevant for software testing professionals

Explores AI-powered testing tools and their capabilities, which helps learners understand how QA automation is evolving with AI intelligence and stay updated in the testing world

Covers generating UI tests using libraries like Selenium, Cypress, and Playwright, which are industry-standard tools for test automation and helps learners develop practical skills

Demonstrates topics using Google GEMINI (AI), which is a cutting-edge AI model and gives learners exposure to state-of-the-art technology

Includes generating API automation tests and SQL queries with simple prompting to AI, which develops skills applicable to backend testing and database management

Discusses integrating AI into existing test automation frameworks, such as Playwright, which helps learners understand how to incorporate AI into their current workflows

Reviews summary

Generative ai for software testers

According to learners, this course provides a practical introduction to applying Generative AI in software testing. Students found it highly relevant to their professional work, covering topics like generating test cases, data, and automation code across various tools such as Selenium, Cypress, Playwright, and Rest Assured. Many appreciated the focus on prompt engineering and using AI tools like Google Gemini. The course is described as well-structured and the demonstrations are particularly helpful. While some wished for more advanced topics or deeper dives, the consensus is that it offers a strong foundation and actionable insights for integrating AI into QA workflows. It's seen as a valuable resource for staying updated in the evolving field.

Addresses a crucial, evolving area in QA.

"This course covers a very current and important topic for anyone in software testing."

"Staying updated with AI is essential, and this course helps bridge that gap."

"I was looking for a course on this specific subject, and this delivered on relevance."

"The content feels cutting-edge and necessary for the future of QA."

Walkthroughs clarify how to apply AI.

"The step-by-step demonstrations using tools like Gemini were very clear and easy to follow."

"Watching the instructor generate code and test artifacts in real-time was insightful."

"The practical demos are definitely the highlight of this course."

"I learned most by watching how the AI tools were actually used for tasks."

Shows AI integration across various automation frameworks.

"It's great that the course demonstrates using AI with different tools like Selenium, Cypress, and Playwright."

"Seeing examples for both UI and API testing with tools like Rest Assured was very helpful."

"The breadth of tools covered gives a good overview of how versatile AI can be."

"Examples across multiple languages/frameworks are a big plus."

Provides a solid understanding of AI in QA.

"The course lays a really good foundation for understanding how AI fits into the testing landscape."

"It covers the basics well and makes complex topics understandable."

"As someone new to AI in testing, I found this course built a great starting point."

"I feel like I have a much better grasp of the potential of generative AI for my role."

Learn immediate ways to use AI in testing.

"This course is highly practical and gives me concrete examples of how to use AI in my daily testing tasks."

"The examples are very relevant to real-world testing scenarios."

"I appreciate the focus on prompt engineering; it's a crucial skill for applying AI effectively."

"This course has provided me with actionable techniques I can implement right away in my job."

Some topics might benefit from deeper exploration.

"While great for an introduction, I wish some sections went into more advanced uses or complex scenarios."

"The course provides a good overview, but I feel like it just scratches the surface on certain tools or techniques."

"Could use more in-depth coverage on optimizing complex automation tasks with AI."

"A deeper dive into the intricacies of specific AI tools would be beneficial."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn Generative AI in Software Testing with these activities:

Review Software Testing Fundamentals

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Reinforce your understanding of core testing principles. This will provide a solid foundation for understanding how AI enhances and transforms these practices.

Browse courses on Software Testing

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Review different testing levels (unit, integration, system, acceptance).
Study testing techniques (black box, white box, grey box).
Familiarize yourself with test design principles.

Read 'AI-Powered Testing: A Practical Guide'

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Gain a broader understanding of AI-powered testing tools and techniques. This will complement the course's focus on prompt engineering and code generation.

View Putting Knowledge to Work on Amazon

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Read the book, focusing on chapters related to AI-powered tools.
Take notes on key concepts and examples.
Compare the book's content with the course material.

Prompt Engineering Practice

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Sharpen your prompt engineering skills through targeted exercises. This will improve your ability to generate effective prompts for various testing tasks.

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Select a specific testing task (e.g., generating unit tests).
Experiment with different prompts to achieve the desired outcome.
Analyze the results and refine your prompts accordingly.
Repeat the process with different testing tasks.

Four other activities

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AI-Assisted Test Case Generation Project

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Apply the prompt engineering skills learned in the course to a real-world testing scenario. This will solidify your understanding and allow you to experiment with different prompting techniques.

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Choose a software project or application to test.
Use AI to generate test cases based on requirements.
Execute the generated test cases and analyze the results.
Refine your prompts based on the test results.

Blog Post: My Experience with AI in Software Testing

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Reflect on your learning experience and share your insights with others. This will help you consolidate your knowledge and identify areas for further exploration.

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Outline the key topics you want to cover in your blog post.
Write a draft of your blog post, including examples and insights.
Edit and revise your blog post for clarity and accuracy.
Publish your blog post on a platform like Medium or LinkedIn.

Review 'The Future of Testing: AI and the Next Generation of Software Testing'

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Expand your understanding of the future trends in AI-powered testing. This will help you stay ahead of the curve and adapt to the evolving landscape.

View Putting Knowledge to Work on Amazon

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Read the book, focusing on chapters related to future trends.
Take notes on key predictions and insights.
Discuss the book's content with other students or colleagues.

Contribute to an Open-Source AI Testing Project

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Gain hands-on experience with AI testing tools and contribute to the community. This will enhance your skills and build your professional network.

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Identify an open-source AI testing project that aligns with your interests.
Review the project's documentation and contribution guidelines.
Contribute code, documentation, or bug reports to the project.
Participate in the project's community discussions.

Career center

Learners who complete Learn Generative AI in Software Testing will develop knowledge and skills that may be useful to these careers:

AI Test Engineer

An AI Test Engineer specializes in applying artificial intelligence to software testing processes. This course is a perfect fit, focusing on prompt engineering for generating tests, using AI-powered tools, and integrating AI into test frameworks. The curriculum includes learning to create test plans, cases, and data with AI and even generating UI tests, API tests, and SQL queries. This course teaches practical skills such as codeless automation with AI QA tools and using AI to parse complex JSON data, directly relevant to the work of an AI Test Engineer. By learning how to use AI to generate test artifacts and automation code, this course prepares learners for the challenges and opportunities in this role.

See salaries and explore the career path for AI Test Engineer

API Tester

An API Tester specializes in testing Application Programming Interfaces. This course is directly relevant to API testers by providing practical skills such as learning to parse complex JSON responses, generate JSON paths, and create API tests using AI. The course also covers generating custom utility code to validate API responses effectively. This focus on API testing with AI, using libraries like Rest Assured and including AI-driven methods, directly applies to the work of an API Tester, who needs to be efficient in how they create tests for API endpoints. The skills in this course will enable testers to tackle modern API testing efficiently. This course will help a learner prepare for an API testing role.

See salaries and explore the career path for API Tester

Software Development Engineer in Test

A Software Development Engineer in Test, or SDET, is involved in both software development and testing. This course directly provides SDETs with skills in generating test automation code using AI tools, which are essential for their role. The course covers how to write automation scripts for various tests, develop custom code, and integrate AI into test frameworks. The skills this course offers, such as generating UI tests, API tests, and SQL queries through AI, helps an SDET efficiently design and implement test strategies. This course is particularly valuable because it directly shows how to use AI to generate complete tests, an essential skill in modern SDET roles.

See salaries and explore the career path for Software Development Engineer in Test

Test Automation Engineer

A Test Automation Engineer designs, develops, and implements automated tests. This course helps aspiring test automation engineers by teaching how to generate test cases, test data, and automation scripts using AI. The course specifically covers generating UI tests using libraries like Selenium, Cypress, and Playwright and creating automation code for API testing, which are crucial skills for this role. This course is especially relevant because it focuses on optimizing code with AI and integrating AI into existing test frameworks, making the test automation engineer more efficient and up-to-date on modern practices. Moreover, the skills in prompt engineering learned in the course allow the engineer to generate effective automation code with AI tools.

See salaries and explore the career path for Test Automation Engineer

Quality Assurance Analyst

A Quality Assurance Analyst is responsible for planning, designing, and executing tests to ensure software quality. This course is a great fit as it provides knowledge in creating test plans, test cases, and test data using AI. The course also explores AI-powered testing tools and self-healing capabilities, preparing a QA analyst for the future of software testing. The course's emphasis on parsing complex JSON responses, using AI to generate JSON paths, and developing API tests are all beneficial for a modern QA analyst. Further, this course helps a QA analyst stay updated on the latest trends in AI-driven testing, making them more effective and efficient in their role and also allows them to generate effective test plans.

See salaries and explore the career path for Quality Assurance Analyst

Automation Architect

An Automation Architect designs and builds automation frameworks and strategies. The course helps build a foundation in using AI to generate automation code, create custom utilities, and configure framework-related files with AI. The course also covers integrating AI into existing test frameworks, important for a test automation architect's role. Specifically, the course’s focus on prompt engineering and optimizing code with AI allows the architect to create more efficient and adaptable automation strategies. This is particularly useful for aspiring architects because it equips them with strategies to leverage AI in test automation, allowing for more advanced and reliable frameworks.

See salaries and explore the career path for Automation Architect

Test Lead

A Test Lead manages testing activities and guides a team of testers. This course is helpful for a Test Lead by providing insights into how AI can optimize testing processes, such as generating test plans, cases, and data efficiently. The course also provides practical skills such as generating UI, API, and SQL tests using AI. Moreover, the course’s curriculum includes learning about AI-powered testing tools and understanding how AI can improve test stability. This enables a test lead to adopt and implement innovative testing strategies using the latest tools and techniques. By learning about prompt engineering and how to use AI to generate test artifacts, a test lead can make better choices for their team.

See salaries and explore the career path for Test Lead

Data Validation Engineer

A Data Validation Engineer ensures data quality through various testing methods. This course is helpful by teaching to generate SQL queries from complex database tables and how to parse complex JSON responses with simple AI prompts. Creating POJO classes for complex JSON files and generating custom utility code to validate API responses are also skills this course covers. Mastering these skills allows the data validation engineer to efficiently perform data validation tasks. By adding AI tools to their skillset, this course sets data validation engineers up for success with modern methods. This course is a good way to learn the AI skills necessary.

See salaries and explore the career path for Data Validation Engineer

Software Tester

A Software Tester executes tests, identifies bugs, and verifies that software meets requirements. This course may be useful by providing practical skills in creating test cases, test data, and automation scripts using AI. The course also enables testers to generate test data combinations, UI tests, and API tests using AI. The skills learned in this course are directly applicable to the daily work of a software tester, and the course will help develop the skills needed to use AI tools. A software tester may find this course helpful to improve their work and testing abilities though this role is less specialized in automation, the course does still have value.

See salaries and explore the career path for Software Tester

Quality Engineer

A Quality Engineer develops and implements quality control processes. This course may be useful, as it introduces AI-powered techniques that help the quality engineer improve testing and quality assurance processes. The ability to generate test plans, cases, and data with AI can enable the quality engineer to refine their methods for quality management. Further, by learning about AI-powered tools, quality engineers can explore how AI might increase the efficiency and efficacy of existing quality processes. Though not fully centered on quality in general, it may provide some benefits.

See salaries and explore the career path for Quality Engineer

DevOps Engineer

A DevOps Engineer is focused on software development and IT operations. This course teaches how to integrate AI into testing and automation, which can be leveraged by a DevOps engineer to help with continuous integration and continuous delivery pipelines. Though primarily focused on testing, this course provides insights into how AI can make test automation more efficient. The DevOps engineer could use this method of automation to streamline testing in their pipelines. Although not a main skill, the AI knowledge is a plus.

See salaries and explore the career path for DevOps Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models and algorithms. While this course is focused on AI in testing, it provides the machine learning engineer with an exposure to prompt engineering and applying AI to automation, which may be useful in their own work. The course teaches how to use AI to generate and optimize code, an important skill in software engineering, which the machine learning engineer would benefit from. While generating tests is not the core work of a machine learning engineer, this course can help bring an AI mindset to their work and provide a new perspecitve.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

A Business Analyst analyzes business needs and translates them into technical requirements. This course may be useful to help business anaylsts communicate their requirements clearly to software teams. The course teaches how to generate test automation code using business analyst requirements, which also allows BAs to understand how their requirements are interpreted. Although not a core set of skills, the ability to create test automation requirements with AI may be beneficial for understanding how software is generated from business requirements. This may be a peripheral benefit.

See salaries and explore the career path for Business Analyst

Project Manager

A Project Manager plans and oversees projects. This course may be helpful by providing insight into how AI tools can improve project workflows and efficiency in software development. While this course is not directly related to project management, it provides exposure to AI in testing and the importance of test automation and how it can impact projects. Understanding this may help a project manager understand how AI can help and what the timeline for this kind of testing may look like. This course provides some insights, though they are not the focus of this role.

See salaries and explore the career path for Project Manager

Technical Writer

A Technical Writer creates technical documentation, such as user manuals and guides. The course may provide a new perspective on how testing workflows can be documented. Although this course does not center on writing, the knowledge in how to create test plans and generate test cases may help provide some background knowledge to someone tasked with documenting such work. It is not a direct fit, but the course does provide insight into the topic the technical writer may be tasked with documenting. It can add some value though it is peripheral to the main function of the technical writer.

See salaries and explore the career path for Technical Writer

Learn Generative AI in Software Testing

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Generative ai for software testers

Activities

Career center

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