AI for Testers

This hands-on course helps testers understand the role of Artificial Intelligence (AI) and Machine Learning (ML) in testing and software quality assurance.

Upcoming Classes

Dates
Mode
Location
Price
Jun 02Jun 03, 2024
Las Vegas, NV
Las Vegas, NV at AI Con USA
$1,545
Call to Schedule
Anytime
Your Location
Your Location
Select a learning mode button (Public, Live Virtual, etc.) for pricing, details, and a downloadable fact sheet.
Description

Artificial Intelligence (AI) has taken the world by storm, increasing the productivity of workers in a wide range of industries, especially software. But, it’s also understandably led to uncertainty and fear about the personal and professional implications for disciplines such as software testing.

If you’re interested in cutting through the hype and understanding how AI affects the testing profession, then this course is for you. In this class, you will gain a solid understanding of AI and Machine Learning (ML), how to test systems with AI components, and how to apply AI to the process of testing itself.

Key takeaways from this class include:

  • Understanding what AI is in its current state along with expected upcoming trends.
  • Leveraging AI to support testing activities like planning, test analysis, test design, implementation, execution, and completion.
  • Effectively testing a system that includes AI components
  • Introducing AI testing tools

Who Should Attend
This course is ideal for those who test AI-based systems or use (or wish to use) AI to support their testing activities. This includes those in hands-on testing roles or test managers, as well as software developers and development managers. In addition, those who want a basic familiarity with these critical topics, such as those in project management, leadership, and consulting roles, will derive value from this course.

Laptop and RDP Required
This class involves hands-on activities using sample software to better facilitate learning. Each student should bring a laptop with a remote desktop protocol (RDP) client pre-installed. Connection specifics and credentials will be supplied during class. Please work with your IT Admin before class to verify that your RDP client can be used to access a virtual machine running in the Amazon Web Services (AWS) environment. If you or your Admin have questions about the specific applications involved, contact our Client Support team.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to AI

  • Definition of AI and AI Effect
  • Narrow, General, and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
    • Contracts for AI as a Service
    • AIaaS Examples
  • Pre-Trained Models
    • Introduction to Pre-Trained Models
    • Transfer Learning
    • Risks of Using Pre-Trained Models and Transfer Learning
  • Standards, Regulations, and AI

Session 2: Quality Characteristics for AI-Based Systems

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability, and Explainability
  • Safety and AI

Session 3: Machine Learning (ML) – Overview

  • Forms of ML
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Workflow
  • Selecting a Form of ML
    • Hands-On Exercise: Selecting a Form of ML
  • Factors Involved in ML Algorithm Selection
  • Overfitting and Underfitting
    • Overfitting
    • Underfitting
    • Hands-On Exercise: Demonstrate Overfitting and Underfitting

Session 4: Testing AI-Based Systems Overview

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
    • Input Data Testing
    • ML Model Testing
    • Component Testing
    • Component Integration Testing
    • System Testing
    • Acceptance Testing
  • Test Data for Testing AI-based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an AI Component
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System
    • Hands-On Exercise: Selecting a Test Approach for an ML System

Session 5: Using AI for Testing

  • AI Technologies for Testing
    • Hands-On Exercise: The Use of AI in Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for Healing or to Create Self-Healing Test Automation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
    • Hands-On Exercise: Build a Defect Prediction System
  • Using AI for Testing User Interfaces
    • Using AI to Test Through the Graphical User Interface (GUI)
    • Using AI to Test the GUI
Dates
Mode
Location
Price
Jun 02Jun 03, 2024
Las Vegas, NV
Las Vegas, NV at AI Con USA
$1,545
Price: $1,545 USD
Course Duration: 2 Days
Description

Artificial Intelligence (AI) has taken the world by storm, increasing the productivity of workers in a wide range of industries, especially software. But, it’s also understandably led to uncertainty and fear about the personal and professional implications for disciplines such as software testing.

If you’re interested in cutting through the hype and understanding how AI affects the testing profession, then this course is for you. In this class, you will gain a solid understanding of AI and Machine Learning (ML), how to test systems with AI components, and how to apply AI to the process of testing itself.

Key takeaways from this class include:

  • Understanding what AI is in its current state along with expected upcoming trends.
  • Leveraging AI to support testing activities like planning, test analysis, test design, implementation, execution, and completion.
  • Effectively testing a system that includes AI components
  • Introducing AI testing tools

Who Should Attend
This course is ideal for those who test AI-based systems or use (or wish to use) AI to support their testing activities. This includes those in hands-on testing roles or test managers, as well as software developers and development managers. In addition, those who want a basic familiarity with these critical topics, such as those in project management, leadership, and consulting roles, will derive value from this course.

Laptop and RDP Required
This class involves hands-on activities using sample software to better facilitate learning. Each student should bring a laptop with a remote desktop protocol (RDP) client pre-installed. Connection specifics and credentials will be supplied during class. Please work with your IT Admin before class to verify that your RDP client can be used to access a virtual machine running in the Amazon Web Services (AWS) environment. If you or your Admin have questions about the specific applications involved, contact our Client Support team.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to AI

  • Definition of AI and AI Effect
  • Narrow, General, and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
    • Contracts for AI as a Service
    • AIaaS Examples
  • Pre-Trained Models
    • Introduction to Pre-Trained Models
    • Transfer Learning
    • Risks of Using Pre-Trained Models and Transfer Learning
  • Standards, Regulations, and AI

Session 2: Quality Characteristics for AI-Based Systems

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability, and Explainability
  • Safety and AI

Session 3: Machine Learning (ML) – Overview

  • Forms of ML
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Workflow
  • Selecting a Form of ML
    • Hands-On Exercise: Selecting a Form of ML
  • Factors Involved in ML Algorithm Selection

  • Overfitting and Underfitting
    • Overfitting
    • Underfitting
    • Hands-On Exercise: Demonstrate Overfitting and Underfitting

Session 4: Testing AI-Based Systems Overview

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
    • Input Data Testing
    • ML Model Testing
    • Component Testing
    • Component Integration Testing
    • System Testing
    • Acceptance Testing
  • Test Data for Testing AI-based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an AI Component
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System
    • Hands-On Exercise: Selecting a Test Approach for an ML System

Session 5: Using AI for Testing

  • AI Technologies for Testing
    • Hands-On Exercise: The Use of AI in Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for Healing or to Create Self-Healing Test Automation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
    • Hands-On Exercise: Build a Defect Prediction System
  • Using AI for Testing User Interfaces
    • Using AI to Test Through the Graphical User Interface (GUI)
    • Using AI to Test the GUI

Class Schedule

Sign-In/Registration 7:30 - 8:30 a.m.
Morning Session 8:30 a.m. - 12:00 p.m.
Lunch 12:00 - 1:00 p.m.
Afternoon Session 1:00 - 5:00 p.m.
Times represent the typical daily schedule. Please confirm your schedule at registration.

Class Fee Includes

• Digital course materials
• Continental breakfasts and refreshment breaks
• Lunches

Instructors

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


Price: $1,495 USD
Course Duration: 3 Days
Description

Artificial Intelligence (AI) has taken the world by storm, increasing the productivity of workers in a wide range of industries, especially software. But, it’s also understandably led to uncertainty and fear about the personal and professional implications for disciplines such as software testing.

If you’re interested in cutting through the hype and understanding how AI affects the testing profession, then this course is for you. In this class, you will gain a solid understanding of AI and Machine Learning (ML), how to test systems with AI components, and how to apply AI to the process of testing itself.

Key takeaways from this class include:

  • Understanding what AI is in its current state along with expected upcoming trends.
  • Leveraging AI to support testing activities like planning, test analysis, test design, implementation, execution, and completion.
  • Effectively testing a system that includes AI components
  • Introducing AI testing tools

Who Should Attend
This course is ideal for those who test AI-based systems or use (or wish to use) AI to support their testing activities. This includes those in hands-on testing roles or test managers, as well as software developers and development managers. In addition, those who want a basic familiarity with these critical topics, such as those in project management, leadership, and consulting roles, will derive value from this course.

Laptop and RDP Required
This class involves hands-on activities using sample software to better facilitate learning. Each student should bring a laptop with a remote desktop protocol (RDP) client pre-installed. Connection specifics and credentials will be supplied during class. Please work with your IT Admin before class to verify that your RDP client can be used to access a virtual machine running in the Amazon Web Services (AWS) environment. If you or your Admin have questions about the specific applications involved, contact our Client Support team.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to AI

  • Definition of AI and AI Effect
  • Narrow, General, and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
    • Contracts for AI as a Service
    • AIaaS Examples
  • Pre-Trained Models
    • Introduction to Pre-Trained Models
    • Transfer Learning
    • Risks of Using Pre-Trained Models and Transfer Learning
  • Standards, Regulations, and AI

Session 2: Quality Characteristics for AI-Based Systems

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability, and Explainability
  • Safety and AI

Session 3: Machine Learning (ML) – Overview

  • Forms of ML
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Workflow
  • Selecting a Form of ML
    • Hands-On Exercise: Selecting a Form of ML
  • Factors Involved in ML Algorithm Selection

  • Overfitting and Underfitting
    • Overfitting
    • Underfitting
    • Hands-On Exercise: Demonstrate Overfitting and Underfitting

Session 4: Testing AI-Based Systems Overview

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
    • Input Data Testing
    • ML Model Testing
    • Component Testing
    • Component Integration Testing
    • System Testing
    • Acceptance Testing
  • Test Data for Testing AI-based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an AI Component
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System
    • Hands-On Exercise: Selecting a Test Approach for an ML System

Session 5: Using AI for Testing

  • AI Technologies for Testing
    • Hands-On Exercise: The Use of AI in Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for Healing or to Create Self-Healing Test Automation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
    • Hands-On Exercise: Build a Defect Prediction System
  • Using AI for Testing User Interfaces
    • Using AI to Test Through the Graphical User Interface (GUI)
    • Using AI to Test the GUI

Class Fee Includes
  • Easy course access: Attend training right from your computer and easily connect your audio via computer or phone. Easy and quick access fits today’s working style and eliminates expensive travel and long days in the classroom.
  • Live, expert instruction: Instructors are sought-after practitioners, highly-experienced in the industry who deliver a professional learning experience in real-time. 
  • Valuable course materials: Courses cover the same professional content as our classroom training, and students have direct access to valuable materials. 
  • Rich virtual learning environment: A variety of tools are built in to the learning platform to engage learners through dynamic delivery and to facilitate a multi-directional flow of information.
  • Hands-on exercises: An essential component to any learning experience is applying what you have learned. Using the latest technology, your instructor can provide hands-on exercises, group activities, and breakout sessions. 
  • Real-time communication: Communicate real-time directly with the instructor. Ask questions, provide comments, and participate in the class discussions.
  • Peer interaction: Networking with peers has always been a valuable part of any classroom training. Live Virtual training gives you the opportunity to interact with and learn from the other attendees during breakout sessions, course lecture, and Q&A.
  • Small class size: Live Virtual courses are limited in small class size to ensure an opportunity for personal interaction.

Bring this course to your team at your site. Contact us to learn more at 929.777.8102.

Dates
Mode
Location
Price
Call to Schedule
Anytime
Your Location
Your Location
Course Duration: 3 Days
Description

Artificial Intelligence (AI) has taken the world by storm, increasing the productivity of workers in a wide range of industries, especially software. But, it’s also understandably led to uncertainty and fear about the personal and professional implications for disciplines such as software testing.

If you’re interested in cutting through the hype and understanding how AI affects the testing profession, then this course is for you. In this class, you will gain a solid understanding of AI and Machine Learning (ML), how to test systems with AI components, and how to apply AI to the process of testing itself.

Key takeaways from this class include:

  • Understanding what AI is in its current state along with expected upcoming trends.
  • Leveraging AI to support testing activities like planning, test analysis, test design, implementation, execution, and completion.
  • Effectively testing a system that includes AI components
  • Introducing AI testing tools

Who Should Attend
This course is ideal for those who test AI-based systems or use (or wish to use) AI to support their testing activities. This includes those in hands-on testing roles or test managers, as well as software developers and development managers. In addition, those who want a basic familiarity with these critical topics, such as those in project management, leadership, and consulting roles, will derive value from this course.

Laptop and RDP Required
This class involves hands-on activities using sample software to better facilitate learning. Each student should bring a laptop with a remote desktop protocol (RDP) client pre-installed. Connection specifics and credentials will be supplied during class. Please work with your IT Admin before class to verify that your RDP client can be used to access a virtual machine running in the Amazon Web Services (AWS) environment. If you or your Admin have questions about the specific applications involved, contact our Client Support team.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to AI

  • Definition of AI and AI Effect
  • Narrow, General, and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
    • Contracts for AI as a Service
    • AIaaS Examples
  • Pre-Trained Models
    • Introduction to Pre-Trained Models
    • Transfer Learning
    • Risks of Using Pre-Trained Models and Transfer Learning
  • Standards, Regulations, and AI

Session 2: Quality Characteristics for AI-Based Systems

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability, and Explainability
  • Safety and AI

Session 3: Machine Learning (ML) – Overview

  • Forms of ML
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Workflow
  • Selecting a Form of ML
    • Hands-On Exercise: Selecting a Form of ML
  • Factors Involved in ML Algorithm Selection

  • Overfitting and Underfitting
    • Overfitting
    • Underfitting
    • Hands-On Exercise: Demonstrate Overfitting and Underfitting

Session 4: Testing AI-Based Systems Overview

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
    • Input Data Testing
    • ML Model Testing
    • Component Testing
    • Component Integration Testing
    • System Testing
    • Acceptance Testing
  • Test Data for Testing AI-based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an AI Component
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System
    • Hands-On Exercise: Selecting a Test Approach for an ML System

Session 5: Using AI for Testing

  • AI Technologies for Testing
    • Hands-On Exercise: The Use of AI in Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for Healing or to Create Self-Healing Test Automation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
    • Hands-On Exercise: Build a Defect Prediction System
  • Using AI for Testing User Interfaces
    • Using AI to Test Through the Graphical User Interface (GUI)
    • Using AI to Test the GUI

Questions?

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