AI in Software Testing: How to Benefit from AI Automation Tools

AI in software testing

As for now, artificial intelligence has not become a magic QA tool that would do all software testing instead of QA engineers. However, it offers sufficient auxiliary capabilities. 

AI automation tools effortlessly generate checklists, test cases and scenarios, but in a rather generalized manner, making them not applicable for in-depth testing of software. Further detailing for a more comprehensive testing should be done by a QA engineer.

AI-generated test cases can give helpful hints regarding the test implementation and emphasize potentially problematic software areas which may require higher attention.

Let’s dive deeper into AI-powered QA and take a closer look at promising AI QA tools.

Freemium AI tools: Chat GPT 4

Chat GPT 4 supports file uploads and downloads. This functionality is extremely useful for fast data analysis and processing, especially when working with large volumes of data. For example, you can quickly find required items, eliminate duplicates, apply filtering, sort andgroup data according to specified categories. 

It’s possible to generate new test data, e.g. names and phone numbers, as well. And what’s great about this - you can ask Chat GPT to create a file with the processed data in a preferred file format, e.g. csv or .JSON, and upload it. Moreover, you can check the ChatGPT code used to complete your tasks. Usually it’s in Python, but you can request to present it in a different programming language.

GitHub Copilot

GitHub Copilot is an AI coding assistant developed by GitHub in partnership with Open AI. You can integrate it directly into the code editor, e.g. Visual Studio Code. Copilot provides real-time suggestions for writing code.

It is a useful test automation instrument as it can

• Generate code based on the comments and existing code
• Automate creating test scenarios boilerplates
• Significantly speed up writing of some autotest components or standard QA solutions.

  

Gemini

Gemini, an AI platform developed by Google, currently doesn’t support work with files, i.e. their uploads, downloads or creation. But it answers your requests almost instantly, being noticeably faster than Chat GPT4. With Gemini you can easily generate test scenarios, test cases, test plans, boilerplate code or some general templates. 

Both Gemini and Chat GPT are great helpers when you need to generate code, e.g. examples of requests for API testing, auxiliary scripts for tests automation or semi automation, etc. Such application of AI in quality assurance can also help you with functions realization for solving your QA tasks.

Qualyfid.ai

Qualyfid.ai can create rather detailed checklists which you can further modify according to the current requirements. They may serve as a good reference for QA workflow, sometimes providing steps one may have not foreseen or have accidently forgotten.

Below is the checklist generated as a result of my request "Edge cases for creating a new user with unique email."

Paid AI tools

There is a large number of AI test automation tools, most of which offer access to their functionality on a subscription-based pricing model. Having no programming skills, there you can get basic QA automation, e.g. generate code or even a complete runner, write a test scenario which you’ll be able to execute via UI panel, etc. Besides, such AI automation tools allow keeping test cases, reports and test results in one app.

Among dozens of paid AI QA automation tools, we have noted a few platforms whose functionality seemed to go beyond the standard feature set.

AskUI

AskUI stood out among other AI software testing platforms with their ability to “see” the tested interface. As a rule, to automate testing of a particular software element, we need to specify its locator, ID, provide CSS or other technical description.

AskAI recognizes interface elements using optical character recognition (OCR) even based on some abstract description, let’s say, provided by someone without technical background. 

Its cross-device support allows interacting with the tested apps without specialized hooks or API. The offered functionality also copes with desktop app testing. Thus, the range of AskAI possible applications is really broad and comprehensive. 

The trial period surely does not provide enough time to spot all the weaknesses one may notice during longer experience. If we assume AskAI’s reliability is high, this AI-powered QA tool could be used for software testing where it’s hard to apply traditional quality assurance automation methods.

Applitools

Applitools is a platform for visual interface test automation. It allows software developers and QA teams to check the app appearance on different browsers and devices. This platform uses artificial intelligence to analyze the images on the screen and detect the differences, if any, with the expected results.

Thus, you can automatically detect the problems related to visual representation, like wrong element alignment, different colors, or scaling issues. It’s a powerful tool for UI testing.

Applitools have their own SDK which you can integrate into frameworks for test automation to increase their effectiveness. 

For instance, Applitools Eyes is easy to integrate into Playwright test scenarios, allowing for adding visual checks without significant changes in code. It’s especially useful for cross-browser testing: since Playwright supports different browsers, its integration with Applitools enables running visual tests in a multi-browser environment, enhancing the QA. 

The parallel testing support allows you to run the tests simultaneously in different environments which significantly decreases the testing time. The UI tests results are conveniently presented on Applitools dashboard.

Applitools SDK integrations with software development and QA automation tools help the teams to quickly find and correct the mistakes, and improve the user interface quality.

Although there are somewhat similar solutions for visual testing, like jest-image-snapshot, their integration and support is more challenging.

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AI testing tools comparison based on their features is shown below.

Pros and cons of AI software testing

All in all, AI provides a variety of new possibilities for QA engineers. Besides its significant assistance throughout the testing process, some AI features can contribute to solving complicated QA tasks. 

To sum up, here’s a brief summary of AI testing tools perks and drawbacks.

Pros of AI for software testing

• Increased time efficiency of quality assurance
• Automated generation of basic tests, checklists and code snippets
• Automatic test data generation
• Basic AQA implementation for non-technical users based on abstract descriptions 
• OCR capabilities for visual testing
• Insightful suggestions for problem solving
• Self-healing tests, granting automated maintenance and constant improvement based on AI learning abilities.

Cons of AI-powered QA

• Lack of ability to create in-depth tests tailored to your particular software 
• Lower reliability compared to man-written tests, mostly because of a chance of being not fully comprehensive. It allows for missing some important aspects.