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Top QA Tools that help to implement AI in Software Testing

Artificial intelligence has impacted software development and the quality assurance process. Secondly, and most importantly, AI has sped up the software quality assurance and delivery process. If we talk about the role of AI in software testing, then the following are the impacts it has on global business:

  • Speed up testing cycles
  • Test cases accuracy improvement
  • Reduce software bugs
  • No dependency on guesswork

By 2030, AI will become a $15.7 trillion industry. Therefore, AI will become one of the fastest-growing technology trends in software development.  Similarly, AI-based QA tools will help the software quality assurance space.

Above all, some of the primary advantages that AI would be giving to QA engineers:

  • Automatically executing test cases by identifying the changes in the code
  • Easily identifying duplicate test cases to optimize test suite
  • Using historical data to predict common test parameters
  • Automatically identifying glitch-prone areas of the software to improve regression testing

But on the other hand to measure the success rate of an AI-based testing tool, you should consider the following traits:

  • Usability

    The tool should be easy to learn and require optimal programming skills. Therefore, it must have tutorials, training, good tech support, and user support.

  • Detailed Documentation

    It must have a robust and detailed documentation process with easy to export and retrieve test results.

  • Functionality and Features

    The tool must support the software testing requirements related to automation, quality, and development.

  • Test Scenarios

    The tool should support multiple test cases and strategies, including performance testing.

  • Supporting Script Languages

    It should support multiple scripting languages and have a deep understanding of one script language.

  • Better Integration

    Firstly, the tool should be easy to integrate with plugins and other tools. Secondly, it must have pre-built integrations capabilities to integrate with bug tracking tools like JIRA. Thirdly, and most importantly, the tool should support multiple browsers and operating systems.

AI-Based QA Tools to Integrate AI in Software Testing

There are plenty of AI-based QA tools with different features and unique capabilities. Above all, let’s take a look at the top QA tools for implementing AI in software testing:

  • TestCraft

It is an AI-enabled automation testing platform for continuous and regression testing. QA experts use this tool along with Selenium. Secondly, TestCraft is suitable for monitory web applications. You can create automated and Selenium-based test cases by using the drag and drop interface. 

In addition, TestCraft helps in identifying web elements by collecting data from all web page elements at a given time. You feed these attributes into the algorithm to determine the element that the tool should be looking for in a given run.

  • Testim

It is an AI-based testing platform used to create end-to-end tests in coded or codeless form, or both. However, the smart locator feature in Testim enables multiple users to work on a single software by allowing them to select and modify a selected item. In addition, it allows testing engineers to create an efficient test architecture by utilizing and reusing proven steps.

  • Functionize

It is a one-stop solution for test creation, execution, analytics, and maintenance. Functionalize is capable of performance, functional, load, and visual testing. Secondly, the tool is fast, easy to use, and can run thousands of tests in minutes from desktop or mobile browsers. In addition, Its engine uses manually created tests and automated test creation techniques to identify assertions, interactions, and inputs.

  • Applitools

Firstly, this tool is highly recommended for visual testing of the applications. Secondly, Applitools helps in finding visual bugs in apps to make sure no visual elements overlap and are invisible or no new unexpected elements may appear. Thirdly, and most importantly, Applitools eyes feature scans the application screen and analyzes them to catch material differences. 

Moreover, the success of a QA tool depends upon how much it simplifies QA engineer’s tasks with traits like:

  • Features & Functionality
  • Value for Money
  • Usability:
  • User Interface (UI)
  • Integrations
  • Documentation
  • Script Languages
  • Version Control
  • Test Commands
  • Test Scenarios


In other words, for integrating artificial intelligence testing with your software development infrastructure, you need a set of reliable and compatible tools that matches your business requirements. QASource uses cutting-edge tools and technologies to provide the best testing solution. In conclusion, to know how artificial intelligence can help you improve the efficiency of the software testing process, contact QASource now.

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