AI and Software Testing Foundation

AI and Software Testing Course + Exam

This is an ideal course for software testers who want to advance their knowledge to testing Artificial Intelligence (AI) systems.

The A4Q AI and Software Testing syllabus is structured in such a ways that it initially covers key aspects of artificial Intelligence, the basics and history of AI, Symbolic AI (humad-readable AI), it covers the limitations of Artificial Intelligence (AI).

Once the AI basics, history and relevant details are covered the second part of the course covers testing Artificial Intelligence (AI) systems. In this part all participants learn different strategies and metrics for Testing AI Systems and the problems which are encountered with the testing.

The A4Q AI and Software Testing Foundation certification verifies an individual possesses the knowledge and understanding of the use of Artificial Intelligence in relation to Testing of AI-based applications and AI for Software Testing.

  • Covers the latest syllabus
  • AI and Software Testing Foundation Usability Testing exam included
  • Exam can be taken within 3 months of course completion

We recommend attending the ISTQB Foundation Certification course before attending AI and software testing course

Candidates can achieve this certification by passing the following exam(s).

  • AIFL: A4Q AI and Software Testing Foundation

The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)

AI and software testing course material included
  • Key Aspects of Artificial Intelligence
  • What are Human Intelligence and Artificial Intelligence?
  • History of AI
  • Symbolic AI
  • Sub-symbolic AI
  • Some ML Algorithms in More Detail
  • Applications and Limits of AI
  • General Problems with Testing AI Systems
  • Machine Learning Model Training and Testing
  • Testing Artificial Intelligence Systems
  • AI Test Environments
  • Strategies to Test AI-based Systems
  • Metrics for Testing AI-based Systems
  • Using AI to Support Testing
  • AI in Testing
  • Applying AI to Testing Tasks and Quality Management
  • AI in Component Level Test Automation
  • AI in Integration Level or System Level Test Automation
  • AI-based Tool Support for Testing

 

This course is likely to add to the employment related skills of the participants. The skills developed are likely to be used in the course of being an employee or working in a business.

  • Developers
  • Testers
  • Test Analysts
  • Test Engineers
  • Test Managers
  • Individuals entering the testing field
  • Individuals seeking the AI Certifications
  • Types of Intelligence
  • Turing Test
  • Main Periods of AI History
  • Difference between Symbolic and Sub-symbolic AI
  • Mathematical Logic and Inference
  • Knowledge-based Systems
  • Constraint-based Solving Systems for Problem Solving
  • Types of Learning
  • Examples of Applications of Different Types of Learning
  • Machine Learning Algorithms
  • Machine Learning Metrics
  • Bayesian Belief Networks
  • Naïve Bayes classifier
  • Support Vector Machine Algorithm
  • K-means Algorithm
  • Artificial Neural Networks: Perceptron Learning Algorithm
  • Activities of Machine Learning
  • Possible Biases in AI Systems
  • Ethical Issues in AI Systems
  • Real-world Inputs
  • Self-optimization
  • Expert Systems
  • Perception of Intelligence
  • Model Optimization
  • AI System Quality Characteristics
  • Bias/Variance Trade Off and the No Free Lunch Theorem
  • Drift
  • Ethical Considerations
  • Automation Bias
  • Adversarial Actors
  • Acceptance Criteria
  • Functional Testing
  • External and Internal Validity
  • Metamorphic Testing
  • A/B Testing
  • Evaluation of Real-world Outcomes
  • Expert Panels
  • Levels of Testing
  • Component Testing
  • System Integration Testing
  • System Testing
  • User Acceptance Testing
  • Confusion Matrix
  • Statistical Significance
  • The Oracle Problem
  • Test Oracles
  • Testing versus Test Automation
  • Tasks AI Can Be Applied To
  • Tasks AI Cannot Be Applied To
  • Using AI for Test Data Generation
  • Using AI for Bug Triaging
  • Using AI for Risk Prediction and Fault Estimation
  • AI in Component Level Test Generation
  • AI in System Level Test Generation
  • Monkey Testing Versus Fuzz Testing
  • AI For Test Generation on the System Level
  • AI For Test Selection and Prioritization
  • AI For Object Identification and Identifier Selection
  • AI For Visual Test Automation
  • Relevant Metrics in an AI-Based Testing Approach
  • Assess Tool Vendor Claims
  • Configuration of the System
  • Return on Investment (ROI)
  • Effects on Existing Processes
  • Sensibility of Test Cases
  • Test Case Explosion
  • Maintainability
  • Severity of the Defects Found
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What is included with the AI and Software Testing Course?
Logitrain course includes official certification exam fee.
Includes Official Exam Fee

Take the certification exam within 3 months of course / module completion

Logitrain's Pass Guarantee
Includes Pass Guarantee

Pass three mock tests with 80% or more for pass guarantee. Full-time courses only

Logitrain courses, programs and packages includes course material.
Includes Books & Meals

Course material in hardcopy is included. Full-time classroom courses include meals

Logitrain courses, packages and programs includes sample practise questions.
Includes Mock Tests

Mock tests included in full-time courses for 3 months from course completion

Logitrain is an authorised test center
Authorised Test Center

Take the official vendor certification exam at the Logitrain training center

Logitrain courses include price beat guarantee.
Price Beat Guarantee

Relax, we will beat competitor’s advertised price. Our course has no extra costs

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