What is the point of our IA and software testing training program ?

The classical approach to software testing is based on a scripted approach.

The creation of these test plans / expected application behaviour requires time. The problem of executing tests, which are usually repetitive, is addressed by test automation tools.

However, tests can still represent a bottleneck in the execution of a project in an Agile environment.

In recent years, the use of “deep learning” and artificial intelligence (AI) has developed in order to provide business solutions based on data analysis to predict behaviour or solve specific technical problems (shape recognition, autonomous driving, business process automation, etc.).

The two issues that arise are therefore:

  • How to test an application that uses AI / Deep learning?
  • How to use AI to help teams test an application differently and save time?

It is to answer these questions that ALL4TEST – in collaboration with the A4Q association – offers a certified training on AI and testing.

Following this training, we also offer, as an option, a coaching / POC tools to help you implement these technologies on your IT projects. Contact us for more information!

A. The content of the IA and software testing training program

1. The outline of the IA and testing training program

Initially, the key aspects of AI such as its history, symbolic AI (i.e. visible to humans) or its limits; are covered in the first of the three main chapters of the Syllabus.

Then, once the prerequisites in terms of AI language are understood, it is time to move on to the testing of AI systems. All participants will then learn about the different measures and strategies to be implemented to test these systems, as well as the issues related to these tests.

Finally, these teachings are completed by modules on:

  • the use of AI for test support
  • the method of applying AI to test tasks and quality management.

The A4Q AI and Software Testing Foundation certification provides a professional level of knowledge and understanding of the use of Artificial Intelligence in software testing and the use of AI in applications that are based on this technology.

2. The objectives of this training program

  • Know the key aspects of Artificial Intelligence
  • Know how to conduct tests of systems that incorporate AI
  • Be able to use artificial intelligence to support testing

3. People who might be interested in this IA and software testing training program

  • Software engineers and testers
  • Designers in general and usability designers in particular
  • Anyone who is interested or involved in AI-enabled software testing and wants to maximize their understanding
  • Business analysts and managers looking to understand how artificial intelligence could add value to the business

B. Details about the A4Q AI and Software Testing certification exam

1. How the exam is administered

This exam is composed by of 40 multiple choice questions, with one point for each correct answer. The requirement for passing this exam is a score of at least 65% or at least 26 questions with a correct answer. The time limit for taking the exam is one hour. Additional time may be granted to candidates whose native language is not the language of the exam (25% or 15 minutes).

2. Recommendations to follow

GASQ’s recommendation for participants is to complete a training program with an accredited provider before taking the exam.

In order to organize your exam, ALL4TEST is at your disposal to help you have all the necessary elements to succeed in your IA and software testing training and your A4Q certification.

Click here to see the Syllabus (English version).

C. Below you will find the summary of the Syllabus for the AI and Software Testing Training


Purpose of this syllabus

Examinable learning objectives and cognitive levels of knowledge

The AI and Software Testing Foundation exam


Level of detail

Organization of this syllabus

Business outcomes


1.0 Key Aspects of Artificial Intelligence


Learning objectives for key aspects of artificial intelligence

1.1 What are human intelligence and artificial intelligence?

Types of intelligence

Turing test

1.2 History of AI

Main periods in the history of AI

Difference between symbolic and sub-symbolic AI

1.3 Symbolic AI

Mathematical logic and inference

Knowledge-based systems

Constraint-based problem solving systems

1.4 Sub-symbolic AI

Types of learning

Examples of applications of different types of learning

Machine learning algorithms

Machine learning metrics

1.5. 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

1.6. Applications and Limitations of AI

Machine learning activities

Possible biases in AI systems

Ethical issues in AI systems

2.0 Testing of artificial intelligence systems

Key words

Learning objectives for testing artificial intelligence systems

2.1 General issues in testing AI systems

Software written to compute an output for which the correct answer is not known

Real-world inputs


Expert systems

Perception of intelligence

Model optimization

Quality characteristics of AI systems

Bias/variance trade-off and no free lunch theorem


Ethical considerations

Automation Bias

Adversarial actors

2.2 Training and testing of machine learning models

2.3 AI test environments

2.4 Test strategies for AI-based systems

Acceptance criteria

Functional testing

External and internal validity

Metamorphic testing

A/B testing

Evaluation of real-world results

Expert Panels

Test Levels

Component Testing

System Integration Testing

System Testing

User acceptance testing

2.5 Test metrics for AI-based systems

Confusion Matrix

Statistical significance

3.0 Using AI to support testing


Learning objectives for using AI to support testing

3.1 AI in Testing

The Oracle Problem

The oracles of testing

Testing versus test automation

3.2. Application of AI to testing tasks and quality management

Tasks to which AI can be applied

Tasks to which AI cannot be applied

Using AI for Test Data Generation

Use of AI for bug triage

Use of AI for risk prediction and failure estimation

3.3 AI in component-level test automation

AI in component-level test generation

AI in system-level test generation

3.4 AI in integration or system level test automation

Monkey Testing Versus Fuzz Testing

AI for system level test generation

AI for test selection and prioritization

AI for object identification and identifier selection

AI for visual test automation

3.5 Support for AI-based testing tools

Relevant metrics in an AI-based testing approach

Evaluate the tool vendor’s claims

System configuration

Return on Investment (ROI)

Effects on existing processes

Test case sensitivity

Test case explosion


Severity of defects found


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