AI in Software Testing Course Overview

This course provides a structured, in-depth exploration of AI’s transformative role in software testing, divided into three comprehensive modules:

  1. Using AI in Software Testing – This module covers how AI can improve various aspects of software testing, from optimizing test case generation to enhancing defect prediction and test maintenance. (goodprompting)

  2. AI-Powered Testing Tools – This module introduces a range of AI-driven tools and techniques available to automate, streamline, and improve the testing process, with a hands-on look at implementing these tools.

  3. Testing AI and Machine Learning Systems – This module tackles the unique challenges and methodologies for testing AI models and machine learning systems, including ensuring model accuracy, fairness, and robustness.

This three-part approach equips testers, developers, and IT professionals with the skills needed to understand, leverage, and test AI in software development environments.

AI in Software Testing Course Outline

Module 2: Using AI in Software Testing

This module introduces participants to the foundational concepts of AI in software testing, focusing on how AI techniques can optimize various testing processes in the Software Development Lifecycle (SDLC). Participants will explore the applications, benefits, and limitations of using AI to improve testing efficiency and accuracy.

Topics Covered:

  • Introduction to AI in Software Testing
  • AI-driven Testplan Generation and Prioritization
  • Genereate testcases for the Requiremtents using AI
  • Generate Playwright script using chatgpt
  • Automate the generation of a playwright script using Chatgpt API, Github api and google colab

Learning Objectives:

  • Understand how AI can enhance software testing processes and improve testing outcomes.
  • Learn about AI’s role in defect prediction, self-healing scripts, and test prioritization.
  • Recognize the benefits and limitations of incorporating AI into traditional and automated testing.

Module 3: AI-Powered Testing Tools

This module delves into various AI-powered testing tools available on the market and examines how to effectively integrate them into testing workflows. Participants will gain hands-on experience with tools that use AI to automate and optimize testing activities, such as test case generation, regression testing, and maintenance.

Topics Covered:

  • Overview of Leading AI-Powered Testing Tools
  • Tool Demonstrations: Setting up and using popular tools like Testim, Applitools, and Mabl
  • Applying AI Tools to Automate Test Case Generation and Maintenance
  • Using AI to Enhance Regression Testing Efficiency
  • Tool Evaluation Criteria: Selecting the right AI-powered tool for specific testing needs

Learning Objectives:

  • Gain familiarity with leading AI-powered testing tools and their specific use cases.
  • Learn to configure and implement these tools in test automation frameworks.
  • Understand how to assess and choose AI tools that align with organizational needs and testing requirements.

Module 4: Testing AI and Machine Learning Systems

In this module, participants will explore the challenges and unique requirements of testing AI and machine learning systems themselves. This includes validating AI model outputs, ensuring reliability and fairness, and testing the robustness of algorithms in real-world scenarios. This module is critical for professionals who need to assess the quality and accuracy of AI models before deployment.

Topics Covered:

  • Unique Challenges in Testing AI and Machine Learning Models
  • Testing for Model Accuracy, Bias, and Fairness
  • Validating Model Performance Across Diverse Data Sets
  • Robustness Testing for AI Models: Ensuring stability under different scenarios
  • Testing Autonomous AI Systems: Quality assurance in self-adapting algorithms
  • Case Studies: Examples of testing AI applications in production

Learning Objectives:

  • Understand the specific challenges involved in testing AI and machine learning applications.
  • Learn techniques to validate model performance, fairness, and robustness.
  • Explore best practices for testing AI models to ensure they meet quality standards in real-world environments.

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Who Should Attend: This course is ideal for: Manual and Automated Testers looking to expand their skills in AI-driven testing. Developers interested in understanding how AI can enhance test automation. Quality Assurance (QA) professionals aiming to optimize their testing strategies with cutting-edge technologies. DevOps Engineers focused on integrating AI into continuous testing and delivery pipelines. IT Managers and Leaders seeking to understand how AI can improve software quality and reduce time-to-market.