Bring AI into your software testing workflow to accelerate test design, automation, maintenance, failure analysis, and risk-based test prioritization.

Artificial intelligence is changing the way software tests are planned, created, executed, and maintained. This course provides testers with practical techniques for introducing AI-powered tools into existing testing workflows without requiring an advanced background in artificial intelligence or machine learning.

Participants will explore how AI can support different stages of testing, from generating realistic test data and creating test cases to prioritizing tests and investigating failed executions. Hands-on exercises use tools such as ChatGPT, Testim, GitHub Copilot, Codeium, Mockaroo, and Faker to demonstrate practical scenarios that testers can apply in their daily work.

The course also covers flaky and redundant test detection, self-healing automation, visual regression testing, and AI-assisted failure analysis. Participants will learn how historical defects, commit activity, code changes, and complexity indicators can be used to identify higher-risk areas and focus testing effort where it delivers the greatest value.

You will also explore how AI-generated tests and insights can become part of existing CI/CD processes using tools such as GitHub Actions. Prompt design techniques are introduced throughout the course to help participants produce more relevant, consistent, and accurate AI outputs.

This instructor-led, one-day course is approximately 50% hands-on. It is designed for testing professionals who already understand fundamental software testing practices but are relatively new to applying AI in their testing activities.



For corporate groups, we can organize this training as an on-site private group.
HAVELSAN
ASELSAN
PepsiCo Türkiye
JTI TÜTÜN ÜRÜNLERİ SANAYİ A.Ş.
ING Bank
DHL
MASTERCARD
Huawei
TotalEnergies
Mercedes-Benz Türk A.Ş.
Goodyear
FORD OTOMOTIV SANAYI A.S.
3M COMPANY
Schneider Electric
Central Bank of Azerbaijan
BP Petrolleri
Procter & Gamble (P&G)
Qatar Foundation
Bank Of China
GlaxoSmithKline (GSK)
MORGAN STANLEY & CO Inc
Novo Nordisk
DEUTSCHE BANK
Azerbaycan Cumhuriyeti Emek ve Ahalinin Sosyal Güvenliği Bakanlığı
ACCENTURE

Prerequisites

Participants are expected to have foundational experience in software testing. Previous experience with artificial intelligence or machine learning is not required.

Recommended knowledge and experience:

  • Ability to read and review test cases

  • Familiarity with functional or UI testing practices

  • Basic experience with manual or automated testing tools and frameworks

Who Should Attend

This course is intended for professionals who want to incorporate AI-assisted techniques into their existing software testing practices.

It is particularly suitable for:

  • Software Testers

  • QA Professionals and QA Engineers

  • Test Automation Engineers

  • SDETs

  • Manual Testers

  • Technical team members involved in software quality processes

No advanced AI knowledge is required. Participants should, however, have a basic understanding of software testing concepts.

What You Will Learn

By the end of this course, participants will be able to:

  • Generate realistic and varied test data with the support of AI tools

  • Prioritize test cases according to code changes, historical test results, and risk indicators

  • Create UI, API, and functional test cases using natural-language instructions

  • Transform user stories and requirements into test scenarios and automation scripts

  • Detect flaky and redundant tests and improve overall test-suite reliability

  • Evaluate self-healing and AI-assisted test maintenance approaches

  • Analyze failed tests and logs with AI to accelerate troubleshooting and triage

  • Identify higher-risk software components using historical defects and code-change data

  • Integrate AI-generated tests and testing insights into existing CI/CD pipelines

Training Outline

1. Introduction to AI in Software Testing

Understand the role of artificial intelligence in modern software testing and how AI can enhance existing testing practices.

  • Understanding AI in the context of software testing

  • Traditional testing compared with AI-augmented testing

  • Benefits of AI for testing speed, coverage, and accuracy

  • Applying AI to unit, integration, UI, and end-to-end testing

  • AI testing tools for different levels of technical experience

  • How AI changes and supports the tester's role

2. Generating Test Data with AI

Learn how to produce realistic, diverse, and structured data for different testing scenarios.

  • The importance of high-quality test data

  • Generating structured data with Mockaroo and Faker

  • Creating edge-case and negative test data with ChatGPT

  • Selecting the appropriate tool for different data requirements

  • Maintaining realism while protecting sensitive information

  • Hands-on: Schema-based test data generation with Mockaroo

  • Hands-on: Generating varied test data through ChatGPT prompts

3. AI-Assisted Test Selection and Prioritization

Use project history and risk indicators to make test suites more focused and efficient.

  • Prioritizing tests according to code and change history

  • Identifying redundant and low-value tests

  • Building smaller test sets while maintaining high impact and coverage

  • Generating test scenarios from requirements

  • Visualizing testing impact through dashboards and analytics

  • Hands-on: Risk-based test case prioritization

  • Hands-on: Detecting redundancy in test descriptions using AI

4. AI-Powered Test Generation and Automation

Turn requirements, user stories, and technical definitions into executable tests with AI assistance.

  • Generating UI, API, and functional test cases with AI

  • Converting user stories into test scenarios and scripts

  • Improving generated results through prompt refinement

  • Exploring GitHub Copilot, Testim, and Codeium

  • Bringing AI-generated tests into IDE-based development workflows

  • Hands-on: Generating UI tests from user stories

  • Hands-on: Creating tests with Testim AI Recorder

  • Hands-on: Generating unit and UI tests with Codeium

5. Intelligent Test Execution and Maintenance

Apply AI techniques to improve test stability while reducing the effort required to maintain automated tests.

  • Automatically identifying and analyzing flaky tests

  • Detecting testing bottlenecks through analytics

  • Working with self-healing selectors and automation flows

  • Applying AI to visual regression testing

  • Tracking changing failure patterns over time

  • Hands-on: Flaky test detection and analysis

  • Demo: AI-assisted visual testing

  • Hands-on: Exploring self-healing test scenarios

6. AI-Based Defect and Risk Prediction

Use historical project information to identify software areas that are more likely to contain defects.

  • Predicting risk from testing and code history

  • Connecting defect patterns with code churn and complexity

  • Applying NLP techniques to commit information

  • Visualizing higher-risk components with heatmaps

  • Using predictive insights during test planning

  • Hands-on: Forecasting defect risk from commit history

  • Hands-on: Identifying high-risk software components

7. Integrating AI into Testing and CI/CD Workflows

Bring AI-assisted testing into everyday QA activities and automated delivery pipelines.

  • Identifying practical AI entry points across the testing lifecycle

  • Using ChatGPT to summarize test results and reports

  • Incorporating AI into CI/CD workflows such as GitHub Actions

  • Using LLMs to analyze and triage failed tests

  • Developing consistent and effective prompting practices for testing

  • Hands-on: Analyzing and summarizing test logs with AI

  • Hands-on: Simulating AI integration within a CI/CD pipeline

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Implementing AI in Software Testing | AI in Test Automation in Denmark through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

  • Delivery Method: Online Instructor-Led
  • Participation Model: Public / Private (In-house)
  • Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
  • Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
  • Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
  • Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
  • Worldwide Access: Join our live virtual classrooms from Denmark or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Implementing AI in Software Testing | AI in Test Automation through face-to-face Classroom Based training in Denmark. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

  • Delivery Method: Classroom Based
  • Participation Model: Public / Private (In-house)
  • Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
  • Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
  • Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
  • Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Implementing AI in Software Testing | AI in Test Automation in Denmark as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

  • Delivery Method: Onsite
  • Participation Model: Private (In-house)
  • Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
  • Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
  • Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
  • Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
  • Worldwide Onsite Delivery: Arrange the training in Denmark or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.
Contact us for more detail about our trainings and for all other enquiries!