7 Best AI Testing Tools for Test Automation

AI has reshaped the landscape of almost every industry around the world, and the software testing industry is no exception. The emergence of AI-powered tools for test automation has changed the way QA teams approach their testing activities. This article walks you through the new ways that you can leverage AI testing tools and lists the top 7 best AI testing tools that can bring your testing game to the next level. These AI-powered tools include:

1. Katalon Platfor
2. TestCraft
3. Applitools
4. Functionize
5. Mabl
6. AccelQ
7. Testim

What Are AI-Powered Testing Tools?

AI testing tools are software testing tools that leverage AI systems to enhance various aspects of the testing process. They assist QA teams in traditional testing activities (test creation, management, reporting, etc.) and perform more advanced cognitive tasks that previously required human intelligence.
 

AI-powered testing tools are a breakthrough in the software testing industry, bringing us closer to level 5 of the Autonomous Testing Benchmark.

Read More: Autonomous Testing: A Complete Guide

The Role of AI in Test Automation

To better understand the role of AI in automation testing, we must first understand the concept of AI — an emerging buzzword.

Artificial intelligence (AI) is the simulation of human intelligence via machines. We have built sophisticated computer programs that can accomplish so many fascinating tasks; however, they are still programs, and they do exactly what they are instructed to do. 

AI is also a computer program, but what makes it different is that it has been trained with enormous volumes of data to the point that it can recognize patterns from the input data and decide the course of action. It can also learn and adapt to new information. This means we can leverage AI for more advanced cognitive abilities, and in test automation specifically, AI can be the game-changer, assisting QA teams in even more advanced tasks.

According to the State of Software Quality Report 2024, test case generation is the most common application of AI for both manual testing and automation testing, followed by test data generation. You can download the report for the latest insights in the industry.

Key Features of AI-Augmented Testing Tools

With AI-enabled test techniques, testers can improve all stages of the Software Testing Life Cycle (STLC).

1. Test Script Generation

Previously, test script creation was a slow process. It takes time for a tester to analyze the requirements, evaluate user stories, and understand the system in-depth to create test scripts. Now, AI and machine learning (ML) can considerably speed up this process by:

• Analyzing not only the requirements but also existing test cases, application behavior, and even testing patterns to uncover insights about the system and create more optimized test scripts.
• Providing predefined templates for common testing scenarios. These templates come with preconfigured code snippets and functions, with detailed comments across the code to help testers get started quickly.
• Generating full test scripts from plain language instructions leveraging NLP techniques.

2. Test Data Generation

Another interesting feature of AI-augmented testing tools is the available and detailed test data. To achieve maximum test coverage, you need a substantial amount of data or an extensive range of test data input to test all possible scenarios. Furthermore, you can leverage AI to perform many data-related tasks, such as:

• Generate synthetic data based on existing test data to serve specific test purposes.
• Transform existing data to create new data variations for more diverse testing scenarios.
• Refine existing data to achieve higher accuracy. 
• Scan through large code bases to better understand the context.

3. Intelligent Test Execution

Test execution often experiences several bottlenecks, such as manually executing test scripts on a wide variety of browsers and environments, or choosing which test cases to execute. AI can offload those tasks for human testers by:

• Automatically handling and organizing test cases in clear categories.
• Efficiently identifying the necessary tests to be conducted across various devices, operating environments, and configurations. 
• Selectively executing regression tests for critical features.

Read More: Understanding The Process of Test Execution

4. Smarter Test Maintenance 

Test maintenance is usually the most challenging part of automation testing. It is time-consuming to have to constantly revise test scripts when a code change occurs, or else the script will not work. AI/ML can minimize redundant testing and failures by:

• Finding the best alternative for any broken selector (known as the self-healing mechanism).
• Analyzing the relationship between UI changes and code changes to identify affected areas.

5. Root Cause Analysis

Root cause analysis with AI helps QA teams find out what's wrong in software testing and understand why it happened by:

• Analyzing the impact areas through logs, performance metrics, reports, and anomalies.
• Tracing issues back to the affected user stories and feature requirements.
• Leveraging existing knowledge bases or repositories to support root cause analysis.
   

Read More: The Potential of AI in Regression Testing

Benefits of AI Testing Tools

AI testing tools offer teams several benefits, including:

• Essentially becoming an assistant for decision-making and analysis capabilities to QA teams.
• Accelerating many testing processes (most importantly test case and test script generation) to shorten release cycle, leading to faster time to market.
• Improving test coverage with instant and customized test data generation.
• Accommodating code changes better.

Best AI Automation Testing Tools

The AI testing tools presented below are at the forefront of the industry, integrating AI technologies into their systems to revolutionize the way we test.

1. Katalon Platform

Katalon Platform is a comprehensive quality management platform that supports test creation, management, execution, maintenance, and reporting for web, API, desktop, and even mobile applications across a wide variety of environments, all in one place, with minimal engineering and programming skill requirements. Katalon comes with many AI features, including:
 

• TrueTest™: One of Katalon’s newest capabilities, can capture real user interactions, model their behaviors in a user journey map, then automatically generate test cases and test data and also measure test coverage on the user journey map.
• StudioAssist: A new feature in Katalon Studio, StudioAssist can turn a plain language input into code (Generate Code feature). You can also select a piece of code and use the Explain Code feature to quickly explain it for all stakeholders to understand.
• Katalon GPT-powered manual test case generator: Seamlessly integrates with JIRA and extracts relevant information from ticket descriptions to create customized and comprehensive manual test cases that align with the specified testing requirements.
• SmartWait: Intelligently pauses test execution until all the necessary elements appear on the screen, lowering the risk of false positives.
• Self-healing: Automatically fixes broken element locators and uses those new locators in subsequent test runs, reducing maintenance overhead.
• Visual testing: Indicates if a screenshot is taken during test execution using Katalon Studio, then assesses the outcomes using Katalon TestOps. AI algorithms are used to identify significant alterations in UI layout and text content that truly affect human users.
• AI-powered test failure analysis: Automatically categorizes failed test cases based on the root cause and suggests appropriate actions.
• Test flakiness: Understands the pattern of status changes from a test execution history and calculates the test's flakiness.
• Image locator for web and mobile app tests (Katalon Studio): Finds UI elements based on their visual appearance instead of relying on object attributes.
• Web service anomalies detection (TestOps): Identifies APIs with abnormal performance.

Here's a quick interactive demo with Katalon Studio:

As one of the pioneers in the use of AI in software testing, Katalon continues to add more exciting AI-powered features to their product portfolio, empowering QA teams around the world to test with unparalleled accuracy and efficiency.

Experience The Power Of AI Testing With Katalon Now

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Learn More About Katalon AI-powered Testing

2. TestCraft

TestCraft by Perfecto is an AI-powered test automation tool based on Selenium. It offers features for both manual and automated testing, excelling at quickly delivering tests for web-based software.

Key features:

• User-friendly interface for constructing automated tests using drag-and-drop capabilities.
• Tests can be executed in parallel across multiple browsers and work environments without the need for strong coding skills.
• TestCraft's On-the-Fly mode generates a test model based on individual test scenarios, simplifying test step reuse.
• ML algorithm to identify web elements accurately, and quickly adapt to changes in the web application.

3. Applitools

Applitools is an AI-based test automation tool that has various applications, but it has been primarily used for testing, monitoring, and managing the visual aspect of apps. The tool is preferred by engineers and digital transformation teams due to its adaptability. It makes effective use of AI and ML to execute visual tests and is recognized for its fast and precise performance.

Key features:

• Effectively identifies visual bugs in apps, ensuring that no visual elements overlap, remain invisible, go off-page, or introduce unexpected features.
• Offers visual test analytics and reporting capabilities.
• The AI-powered feature Applitools Eyes accurately detects material differences and distinguishes between relevant and irrelevant ones.

Check out a G2's feature comparison between Katalon and Applitools. G2 is the world's leading tech review site, and this comparison table is based on authentic user reviews.

4. Functionize

Functionize is a cloud-based automated testing tool used for functional, performance, and load testing, providing a comprehensive solution. This tool incorporates AI/ML technology to accelerate test creation, diagnosis, and maintenance.

One notable advantage of Functionize is its simplicity. You can effortlessly perform tests by typing your desired test scenarios in plain English, and the tool's natural language processing (NLP) generates functional test cases. Additionally, it efficiently executes a large number of tests within minutes across various desktop and mobile browsers. If you're seeking a test automation tool, Functionize is definitely worth considering.

Key features:

• AI and NLP-based automation testing
• Test analytics and reporting tool for monitoring test results and software functionality
• Self-healing capabilities
• Test planning and editing capabilities
• Multiple browser support tools

5. Mabl

Mabl has a powerful and easy-to-use graphical user interface (GUI). It is an AI testing tool that enables low code testing without the need for scripting. This tool is ideal for busy teams as it allows high-speed testing throughout the entire software development process. Additionally, Mabl promotes collaboration between clients and managers at every phase.

Key features:

• Low code test capability to prioritize quality
• Intuitive intelligence and automated healing
• Data-driven capabilities for real-life test cases
• Comprehensive end-to-end testing via APIs
• Generates valuable insights for developers using data
• User-friendly dashboard for easy navigation

6. AccelQ

Accelq is an AI testing tool that automates test designs and plans, and executes them. It is beneficial for testing UI, mobile, API, and PC software, providing a comprehensive view of the entire QA lifecycle.

Key features:

• Automated test generation capabilities
• Intuitive dashboard included
• Features for predictive analysis and lifecycle test management
• API and comprehensive test view from A to Z

7. Testim

Testim Automate is a test automation platform that employs machine learning to tackle two common challenges in software testing: slow test creation and extensive test maintenance. With Testim, individuals without coding skills can quickly create end-to-end tests using its recording functions. Engineers can also leverage code to enhance these capabilities, combining the strengths of both approaches.

Testim enhances test maintenance with its innovative smart locators method. This method examines every element utilized in testing and assigns weights to hundreds of attributes for each element. Therefore, even if one attribute, such as an ID, changes, Testim can still locate the element, preventing test failures. The best part is that this process is straightforward, eliminating the need for complex and error-prone queries.

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