Future-oriented AI automated testing tools

​Translator | Chen Jun

Reviewer | Sun Shujuan

In recent years, automated testing has undergone major iterations. It assists the QA team in reducing the possibility of human errors to a great extent. Although there are many tools that can be used for automated testing, the right tool has always been the key to the success or failure of automated testing. At the same time, with the widespread use of artificial intelligence, machine learning and neural networks in various fields, automated testing for artificial intelligence also requires appropriate tools to undertake repetitive work, so as to save valuable time of the project team and perform more precise tasks. Complex and critical tasks. Below, I will discuss with you in depth the future-oriented AI automated testing tools.

What is artificial intelligence (AI) automated testing?

AI automated testing means that existing software automation testing tools utilize artificial intelligence and machine learning, to produce improved results and eliminate common challenges faced by automated testing.

Although AI automated testing can help speed up product life cycles and increase an organization's revenue, it is still in its early stages and requires continuous improvement to meet specific and independent automated testing needs in different scenarios.

How does artificial intelligence assist automated testing?

Typically, testers need to maintain and modify thousands of test cases. AI automated testing tools can smoothly accelerate automated testing on the basis of providing transparency by handling repetitive tasks, generating relevant data required for decision-making, and detecting and correcting discovered problems in the early stages of the development life cycle. Processing. At the same time, AI also helps maintain automated test suites and simplify unit, UI and API testing. Therefore, we can summarize the following characteristics:

• AI testing tools can detect and fix errors at an early stage. For areas that need to be changed in the application, AI will use its intelligent technology to dynamically modify the script to relieve testers of the tasks of maintaining test cases.
• AI automation tools help make processes more efficient and transparent.
• Such tools can also improve the quality of test output with greater accuracy and speed.

Generally speaking, AI will help authorize, execute and maintain automated tests, and improve the efficiency of the QA process by generating relevant data in advance to make decisions, detect and correct errors.

Advantages of Artificial Intelligence in Software Automation Testing

AI can enhance the scope of testing capabilities and change the way test equipment works through the amplification mechanism. Compared with traditional methods, AI-oriented automated testing can learn, adapt, execute, improve and decide various complex problems like humans, without too much manual intervention. The changes brought about by AI in improving testing efficiency and overcoming automated testing challenges are transformative. They mainly target the following pain points:

Limitations of professional knowledge

In the past, some open source tools and inappropriate automated testing tools often required testers to have above-average testing skills in order to create Automated test suite. But in fact, not every tester has the programming skills and background knowledge required for the project.

Continuous Maintenance

Automated test suites must be regularly updated with patches and new features for their corresponding products. Even with advanced automated testing tools, ongoing maintenance is inevitable. For example: Refactoring test cases is a common UI automation challenge. Within days of being created and tested, they require regular maintenance to ensure their suitability.

Test Reporting

Many automated testing tools either fail to provide reporting capabilities (most of them are open source after all) or provide less information and no comprehensive insight analysis. If you want to add additional reporting capabilities, you will need to do custom programming or use external plug-ins.

Scalability

As the number of automated test suites grows, the automation framework needs to support a large number of tests and be able to provide test results quickly and in a parallel manner. In addition, in order to execute tests smoothly, we also need to prioritize tests to execute tests on different devices and configurations.

It can be seen that in order to meet the above needs, it is extremely challenging to choose the appropriate automated testing tool. Therefore, we thought about whether AI can give appropriate help.

Future-oriented AI automated testing tools

Before the QA team determines what kind of AI automated testing tools they need to choose, they need to understand what effects such tools can typically achieve. Here, I summarize it for you as follows:

1. Can perform predictive self-healing. That is, the test suite is automatically updated as the application iterates.
2. Perform intelligent defect capture (Bug Hunting). That is, intelligently discover errors through AI-driven testing mechanisms. This mechanism can crawl the entire application, detect issues, and fix them.
3. Enhance the robustness of your application by applying predictive auto-scaling and continuous fitness functions.
4. It’s not just a simple automated test, it can also automate business processes and workflows that perform various end-to-end tests.

The following are AI automation tools that QA teams should pay attention to:

Applitools

• It is a trustworthy AI automation tool for visual UI testing and monitoring.
• As the only tool driven by visual artificial intelligence (Visual AI), it allows machines to imitate human eyes and brains, providing recognition functions and various regressions.
• Its Applitool Eyes tool can analyze the entire screen of an application with just one line of code.
• It utilizes artificial intelligence and machine learning for test maintenance. Its comparison algorithm can determine whether an application's changes were intentional or an error.
• It reduces the steps to write and learn new tests and extends existing tests by smoothly integrating with existing tests.

Testim.io

• Testim is an end-to-end AI testing tool that can be used to write, execute and maintain automated tests and run multiple tests in parallel, to reduce the time from test creation to execution.
• Its advantages are mainly reflected in functional testing and UI testing.
• It overcomes testing problems such as slow and unstable builds in the UI caused by frequent changes and releases.
• Its intelligent locator can detect changes in the application and run automated tests on demand.
• Through seamless integration with CI/CD tools, it can provide detailed error reports and perform root-cause analysis on failed tests to quickly take various remedial measures.

Mabl

• As a leading AI automation testing tool, it is designed to create and run tests across CI/CD.
• Mabl's Google Chrome extension helps developers create scriptless tests to create and run tests on Firefox.
• It is a cloud-based tool with self-healing and visual testing capabilities.
• Mabl uses machine learning algorithms to detect problems or threats in code and improve test execution.

Parasoft SOAtest

• It is an AI automation tool that integrates API and Web services. It provides: API testing, WebUI integration, mobile testing, load testing, performance and various end-to-end functions such as API security testing.
• Its intuitive interface automates testing of a variety of key issues related to API, load, performance and security.
• It simplifies the maintenance tasks of testing by providing continuous analysis of changes and their impact.
• Its testing technology can provide real test data for further modeling, masking and generating additional data.
• Easily coordinate the execution of multi-pass tests directly in the browser for continuous testing.

TestProject

• This AI testing tool simplifies testing work by reducing the trouble of test setup, maintenance, and connecting servers and drivers.
• It is equipped with built-in automated assistant, AI self-healing, and adaptive waiting functions.
• It comes with an AI-enabled codeless test recorder that is compatible with the open source SDK--Selenium API.
• It also comes with an additional library, special test reports, dashboards, and integration with CI/CD pipelines.

AccelQ

• As a cloud-based codeless AI automation testing tool, it focuses on automating WebUI, API, desktop and mobile platforms.
• It comes with natural English programming, intelligent element browser, automatic test generation, and self-healing functions.
• It integrates well with popular DevOps tool chains to provide a unified view of the complete QA life cycle.
• It provides predictive and path analysis for developing test scenarios and maximizing test coverage model UI and data flow. Among them, open source alignment is another distinguishing feature of this tool.

Functionize

• This tool belongs to the declarative category of AI testing tools. As a cloud-based automated testing tool, it uses machine learning and artificial intelligence to create, validate, and maintain tests.
• Its AI-driven intelligent agents can quickly create tests and use natural language to handle English-based processes.
• Its SmartFix feature can easily detect UI changes and test failures.

TestCraft

• This is an AI-driven testing tool for regression and continuous automated testing.
• It uses machine learning algorithms to correctly identify various web elements during application changes.
• It enables testers to intuitively create Selenium-based automated tests and run these tests across multiple browsers and platforms.
• Its On-the-Fly mode can create test models outside of test scenarios, making it easier to reuse test steps.
• Have certain self-healing ability.

Summary

Nowadays, AI has already entered the field of software testing. Various AI testing tools already have machine learning and deep learning capabilities that cannot be covered by traditional automated testing. Although they cannot completely replace manual testing yet, they can already supplement and accelerate existing automated testing needs and processes.

Translator Introduction

Julian Chen, 51CTO community editor, has more than ten years of experience in IT project implementation. He is good at managing and controlling internal and external resources and risks, and focuses on disseminating network and information security knowledge. and experience; continue to share cutting-edge technologies and new knowledge in the form of blog posts, special topics, and translations; often conduct information security training and teaching online and offline.

Original title: ​​Top Powerful AI Test Automation Tools for the Future​​​, author: Priyanka Charak user avatar​

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