A4Q AI and Software Testing

A4Q AI and Software Testing
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There are many similarities between Artificial Intelligence and human intelligence for one simple reason: Artificial Intelligence aims to mimic human intelligence.  The term Artificial Intelligence dates back to the 1950s and refers to the objective of building and programming “intelligent” machines capable of imitating human beings.



As AI systems become more human, they become less predictable, and there are unique problems and quality characteristics, which need to be considered when testing such systems.



The first of three main chapters of the A4Q AI and Software Testing Syllabus teaches all Key Aspects of Artificial Intelligence, including the history of AI, Symbolic AI (humad-readable AI) as well as where the limits of AI can be found.

After everyone is speaking the AI-Language the path continues to Testing AI Systems. In this part all participants learn different strategies and metrics for Testing AI Systems as well as the general problems which come along with the testing.

Using AI to Support Testing and how to apply AI to testing tasks and QM completes the Syllabus of the A4Q AI and Software Testing Scheme.


Rex Black about AI and Software Testing


 



 

Trainingsvorm

1,795.00

(excl. BTW)

Trainingsduur

3 dagen

Afronding

Certificaat van deelname

Startdatum

05 januari 2021

Locatie

Startel (Drachten)

Welke voorkennis heb je nodig?

There are no prerequisites for taking the A4Q AI and Software Testing Exam.

Kom je er niet uit?

Neem dan contact op met onze klanten service voor studieadvies of een training op maat.

 

Trainingsdata

Startdatum

Trainingsvorm

Locatie

Duur

Prijs

datum:
05 januari 2021

Klassikaal

Startel (Drachten)

3 dagen

1,795.00

datum:
31 maart 2021

Klassikaal

Startel (Drachten)

3 dagen

1,795.00

datum:
31 maart 2021

Klassikaal

Startel (Drachten)

3 dagen

1,795.00

datum:
09 juni 2021

Klassikaal

Startel (Drachten)

3 dagen

1,795.00

datum:
16 augustus 2021

Klassikaal

Startel (Drachten)

3 dagen

1,795.00

datum:

In company

Waarom Startel?

Persoonlijke
aanpak
Ervaren
trainers
Aanbod
op maat
Klassikaal en e-learning
mogelijkheden

Het programma

Learning Objectives for 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 


What are Human Intelligence and Artificial Intelligence?


  • Types of Intelligence 
  • Turing Test 


 History of AI


  • Main Periods of AI History
  •  Difference between Symbolic and Sub-symbolic AI


 Symbolic AI


  • Mathematical Logic and Inference  
  • Knowledge-based Systems
  • Constraint-based Solving Systems for Problem Solving 


Sub-symbolic AI 


  • Types of Learning
  • Examples of Applications of Different Types of Learning
  • Machine Learning Algorithms
  • Machine Learning Metrics


Some ML Algorithms in More Detail


  • Bayesian Belief Networks
  • Naïve Bayes classifier
  • Support Vector Machine Algorithm 
  • K-means Algorithm 
  • Artificial Neural Networks: Perceptron Learning Algorithm 


Applications and Limits of AI 


  • Activities of Machine Learning
  • Possible Biases in AI Systems 
  • Ethical Issues in AI Systems 


2.0 Testing Artificial Intelligence Systems 


  • General Problems with Testing AI Systems 
  • Machine Learning Model Training and Testing 
  • AI Test Environments 
  •  Strategies to Test AI-based Systems 
  •  Metrics for Testing AI-based Systems 


 General Problems with Testing AI Systems 


  • Software Which Is Written To Calculate an Output for Which the Correct Answer Is Not Known 
  • 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


Machine Learning Model Training and Testing 


  • AI Test Environments
  • Strategies to Test AI-based Systems 
  • Acceptance Criteria 
  • Functional Testing 
  • Levels of Testing 


Metrics for Testing AI-based systems 


  • Confusion Matrix
  • Statistical Significance


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


AI in Testing 


  • The Oracle Problem 
  • Test Oracles 
  • Testing versus Test Automation 


Applying AI to Testing Tasks and Quality Management 


  • 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 Automation 


  • AI in Component Level Test Generation 
  • AI in System Level Test Generation 


AI in Integration Level or System Level Test Automation 


  • 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 


AI-based Tool Support for Testing 


  • 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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