Software Test Automation with Artificial Intelligence

Artificial Intelligence in Software Test Automation is a discipline that employs artificial intelligence (AI) and machine learning (ML) techniques to make software testing processes more efficient, intelligent, and effective. The primary goal is to reduce the time and effort spent on manual testing, expand test coverage, and enable test engineers to focus on more complex and innovative tasks. AI automates and optimizes testing processes by using computer programs endowed with learning and reasoning capabilities, simulating human intelligence. This technology uses tools known as “test bots” to automate activities such as application discovery, modeling, test generation, and defect detection.

Historical Development and Core Concepts

The foundations of artificial intelligence stem from the work of computer scientist Alan Turing in the 1940s and 1950s, particularly his Turing Test, which aimed to determine whether a machine could imitate human intelligence. The widespread adoption of AI across diverse industries such as manufacturing, food, metallurgy, and automotive has also led to its integration into information technology and software development. In particular, AI applications have triggered a significant transformation in the software testing domain. Since 2014, there has been a marked increase in the number of companies offering AI-based test automation services.

The foundation of AI-driven test automation lies in machine learning and neural networks. Machine learning enables computers to classify objects or make probabilistic predictions based on existing data. Neural networks provide flexible learning models by mimicking the way the human brain forms associations. These technologies enable test bots to operate effectively even under uncertain conditions, offering more robust solutions than classical test automation frameworks. Over the past decade, three main disciplines have emerged in this field:

• AI-Driven Testing: Development of AI tools specifically for software testing processes.
• Testing AI Systems: Development of methods to test AI-based systems themselves.
• Self-Testing Systems: Development of systems capable of testing and adjusting themselves autonomously.

The Role and Applications of Artificial Intelligence in Software Testing Processes

Artificial intelligence can be applied at nearly every stage of the software development lifecycle’s testing phase, offering solutions from test planning to reporting.

Test Planning, Analysis, and Design

AI supports the planning, analysis, and design of testing processes. It can be used to identify, prioritize risks, and formulate test strategies.

Test Scenario and Data Generation

Generative AI and Large Language Models (LLMs) have revolutionized test automation. These technologies can automatically generate test scenarios from natural language instructions or requirement documents written in languages such as English. This approach increases participation in the test automation process from stakeholders without technical expertise, such as business analysts and domain experts. Additionally, AI rapidly produces synthetic and complex test data that conform to specific formats or rules, enabling more comprehensive testing.

Codeless Automation

Many AI-supported tools enable test automation without requiring complex programming knowledge. Users can define test steps through simple commands or interfaces. The use of languages such as Gherkin^【1】 facilitates this process by making test scenarios understandable to both humans and machines.

Self-Healing

One of AI’s most important capabilities is self-healing automation systems. When changes occur in an application’s user interface (UI), traditional automation scripts^【2】 typically fails and requires manual maintenance. AI-supported tools, however, automatically detect these changes and update test scripts, significantly reducing maintenance costs and effort.

Visual Testing

AI plays an effective role in visual consistency testing, where traditional automation tools fall short. By comparing screenshots of application screens, AI can detect errors in visual elements such as color, layout, and typography.

API, Load, and Stress Testing

AI can automate API (Application Programming Interface) testing and generate synthetic API calls to verify whether integrations function correctly. Additionally, it can perform real-time load and stress tests simulating millions of users to measure how systems respond under extreme demand.

Exploratory Testing

AI can assist test engineers in organizing exploratory testing sessions, identifying risks, and detecting defects during testing.

AI-Enabled Testing Tools and Platforms

The market offers a variety of AI-supported test automation tools and platforms tailored to different needs. These tools are typically delivered via the SaaS (Software as a Service) model and can integrate with CI/CD (Continuous Integration/Continuous Delivery) pipelines.

• testRigor: A generative AI-based tool that allows users to write tests using simple English commands. It offers features such as self-healing, two-factor authentication (2FA) support, and API testing.
• CoTester (TestGrid): Defined as an trainable and taskable AI testing tool capable of autonomously executing complex testing tasks. It supports codeless automation and parallel test execution.
• Text2Test: An initiative targeting non-technical stakeholders by enabling them to participate in the testing process. It uses Large Language Models (LLMs) to interpret test scenarios written in natural language and convert them into executable tests.
• Robotic.mobi: A platform that enables testing of mobile applications, websites, and APIs without writing code. It provides a real device lab, eliminating the need for companies to purchase physical devices and significantly reducing test duration through parallel test execution.
• Qualiti.ai: Aims to manage the entire process of writing, maintaining, and reporting tests in a fully autonomous manner without human intervention.

These tools typically build upon open-source technologies such as Playwright, Selenium, and Cypress, and can integrate with popular development tools like Jenkins, Bamboo, and Jira.

Benefits and Objectives of AI-Powered Test Automation

The integration of AI into test automation offers numerous strategic advantages for software development organizations.

• Speed and Efficiency: Can accelerate testing processes by up to tenfold. For example, a testing process that requires 100 person-days when performed manually across 100 devices can be reduced to just one person-day through parallel test execution, significantly shortening time-to-market.
• Increased Coverage: Can expand test coverage by up to 100 times by encompassing numerous scenarios and data combinations that are difficult or impossible to test manually.
• Cost Reduction: Can reduce engineering expenses by up to 30%. It also eliminates the need to establish and maintain physical device labs, resulting in hardware cost savings.
• Quality Improvement: Enhances software quality through more precise defect detection and self-healing capabilities. Continuous testing and monitoring enable potential issues to be identified at early stages.
• Accessibility and Collaboration: Codeless and natural language-based tools make the testing process understandable and accessible not only to software teams but also to product, analysis, marketing, and even customer teams.

Position in the Industry and Standardization

The field of AI-supported test automation is a rapidly growing market attracting significant investment. Investments such as $495,000 in Text2Test and $6.5 million in Qualiti.ai demonstrate the sector’s potential and investor interest. As the industry matures, efforts are being made to standardize knowledge and skills in this domain. The ISTQB® Certified Tester AI Testing (CT-AI) certification offered by organizations such as the Turkish Testing Board (TTB) is an indicator of professionalization in this field. Such advanced certifications typically require a prerequisite of foundational testing knowledge, such as the ISTQB® Foundation Level. Additionally, specialized training programs and workshops focused on AI-supported software testing are being organized to help companies and individuals adapt to these new technologies.