AI Software Testing and AI QA

Testing today has reached a whole new level thanks to automation and the involvement of AI technologies. Get rid of constant manual work, long software checks, and lots of paperwork. AI has simplified these processes and become a prime assistant for testing teams. In this article, you’ll find out how AI has changed the testing process, what you should know before using it in your project testing, and what benefits this technology will bring to your business. A lot of useful data awaits you in the following sections.

AI tools for software and QA testing

We’ve talked a lot about the impact of AI on the development process, design, and code writing. Now, it’s time to talk about its capabilities in software testing. Automation of testing processes has become a common thing for teams, and AI technology is a must-have in this process today. Let’s start with well-known AI tools, as they provide fast and accurate product testing. Here they are.

• Testim: This popular tool analyzes system behavior, examines tester interaction with the app, and creates automated tests based on these actions. Over time, the need for manual intervention decreases, and the accuracy of testing reaches a new level. Testim is a perfect choice for large-scale projects where teams need to create and test event scenarios quickly.
• Applitools: Specializes in visual testing to detect graphical defects in user interfaces. The tool compares images at different development stages, finds any deviations, and reports mistakes. This technology helps to avoid all the issues with the elements displaying on different devices and is a fundamental thing for UI/UX testing.
• Mabl: This AI tool analyzes code and the development process. It identifies potential bugs, helps to create automated scripts, and improves these scripts immediately in the process. One of the main advantages of Mabl is the ability to test web applications simultaneously in different browsers.
• Functionize: Using AI, it creates tests through a visual interface. Thus, it speeds up the testing process and facilitates the work even for those without deep technical knowledge. This tool is an attractive option for startups or companies that need to deploy automated tests quickly.
• Sauce Labs: It’s a cloud-based testing platform that lets you run automated tests on different devices and browsers. Its AI algorithms analyze test results and help identify any existing issues. In turn, this will save you time analyzing mistakes and make quality assurance more efficient.

We recommend you try out the features of each of these tools to find the one that’s right for your project. But keep in mind that some of them are free, while others require a paid subscription. Either way, they will become essential tools for your QA teams, no matter what project you’re working on. 

What’s the best way to improve software testing with AI?

First, familiarize yourself with the AI tools from the previous section and choose the most suitable option for you. Knowing your project specifics, you can easily make this choice. The next step is to implement them and other automated tools with built-in AI technology to analyze code, find mistakes, and optimize any test scenarios. What else? Let’s find out.

Integrate AI tools into existing DevOps processes. This way, testing will be continuous and adaptive to changes in the system. Also, AI technologies can improve the product’s visual interface. They are programmed to find mistakes and deviations in the existing interface and provide recommendations on how to fix them. What’s the result? Not only clean code but also an attractive design without any mistakes in its display.

Besides, AI will be very helpful in analyzing test results and predicting potential bugs. So, put it into this process as well. It doesn’t just signal current bugs, it also predicts those that may appear. It’s a kind of portable harbinger that will always help you to prevent bugs.

In order to maximize the effect of all AI capabilities, it’s worth training QA and development teams to work with them properly. Implementing this essential technology will reduce mistakes, accelerate the release process, and guarantee high product quality. There are a lot, right?

In what ways is generative AI changing software testing?

Without any long introductions, let’s get straight to the point. Generative AI really changes the software testing process significantly. And now we’ll figure out how exactly. What does it do?

Automatically generates test scenarios

AI can automatically create a large number of test scenarios based on code analysis and user behavior. This significantly saves QA teams time and ensures a fast and comprehensive testing process.

Predicts mistakes

Generative AI is able to analyze historical data and predict possible mistakes or issues that can be expected in the future. This way, teams can detect them early in the development process and ensure clean code for the product.

Optimizes regression testing

AI can identify key areas that need retesting after changes have already been made. This approach reduces the time spent on repeated code checks and significantly increases testing efficiency.

Conducts testing based on simulations

AI can create simulations of various software usage scenarios, such as how the interface will be displayed, what main functions users will use, which buttons will be clicked, etc. Thanks to the data obtained, teams will be able to identify all the issues that may arise in the product’s actual use.

Analyzes test results

Generative AI can automatically analyze test results and identify the most critical bugs. What does it do with it next? Based on the gathered data, AI creates a list of recommendations for prioritizing issues and provides them to development or QA teams.

So, we can confidently say that generative AI makes the testing process faster, more accurate, and more adaptive. This really helps to improve the software quality and ensure its proper functioning even before the release.

Here’s what you need to know before doing AI testing

In order to get the best test results, you need to consider a few simple but important things. Let’s analyze each of them all alone.

• Knowing how AI models work is important. This is the first thing to note before starting testing. The more you know about their features and possible nuances, the easier it will be to evaluate the test results and identify potential issues.
• AI models require a large amount of high-quality data for training. In fact, further work with AI as a testing tool depends on it. The materials you provide will affect how the product works. Therefore, it’s extremely important to make sure that the input data is accurate, diverse, and up-to-date.
• AI allows automating most testing processes. But don’t assume that it can replace the entire testing process. Leave the more complex stages of testing to QA teams that can do this work with particular precision.
• Since AI can work on a self-learning basis, sometimes the results can be unpredictable. In this case, additional control over the accuracy of conclusions should be organized with the QA team or developers’ contribution. This will increase the chances of a quality check and eliminate any mistakes and failures that could harm the product.

Even so, the main thing you should keep in mind is that AI testing requires very detailed preparation. Consider any force majeure, possible product scenarios, etc. Of course, this requires additional analysis and time, but it will significantly improve the final product quality. That’s the whole point of testing, right?

How do bots help software testers?

Not every company sees the non-obvious benefits of using bots. These are small assistants that speed up the teams’ workflows and simplify certain processes. If we compare them to AI tools, they are more like individual microorganisms, because they have specific programs to perform one core task. In contrast, AI is a universal tool that performs lots of tasks prescribed by QA teams.

Here’s how bots help teams speed up the software testing process. They do the following.

• Automate routine tasks: Bots can automate numerous routine tests, such as regression testing, user interface testing, and functional testing. This will save companies a lot of time and resources.
• Quickly check tons of data: Bots quickly analyze huge amounts of data and run scalable tests. Why is this important? Because it reduces the likelihood of mistakes that may go unnoticed during manual testing.
• Provide support for continuous testing: They can run 24/7, which lets you test continuously. This is especially useful in CI/CD (continuous integration and delivery) processes.
• Create user behavior simulation scenarios: Bots simulate user actions in different scenarios. It means, which button will be clicked, which page will be visited, etc. This practice helps to identify issues that may arise in the actual program use. QA teams use this approach a lot because it helps them see a little further.

Here, we’d like to highlight two central perks of using bots for software testing. First, you can speed up the product testing process by doing small tasks or participating in larger ones with bots’ help. The second is a reduction in the likelihood of human mistakes. Since bots can see the system from the inside, they can find weaknesses faster. Just those reasons are enough to implement these quick fixes.

Pros and Cons of using AI in software testing

Like any tool or system, AI technology also has its drawbacks and advantages. To help you decide whether to use AI in project testing, we’ve outlined the most common ones. Check them out. 

Business benefits from AI software testing

We’ve already described in detail the advantages of using AI in software testing. In this section, we’ll talk about the benefits that businesses will get from its implementation. As you know, success has two components: the product you are working on and your business as a whole. Let’s talk about it. 

First of all, the automation of routine processes, that testing requires, lets you enter the market with a finished product faster. At the same time, the company’s expenses are reduced, as testing with AI tools reduces the need for human resources and QA processes. Also, this innovative technology is easily adaptable to project scaling, so it’s much easier for QA teams to expand testing of functions or user behavior without hiring additional people.

Since AI detects more bugs and defects by analyzing behavioral patterns, we have the next benefit – increasing product quality. For a company, this means brand credibility and a competitive advantage in its industry. Also, by analyzing an unlimited number of data, AI can predict financial and technical risks in the early working stages. This provides businesses with a certain financial security, as there is no possibility of additional costs due to mistakes corrections, or product quality upgrades. 

In short, the use of AI for software testing helps your business cut costs, improve product quality, accelerate its entry into the market, and increase competitiveness. Therefore, it makes sense to think about implementing AI in your project QA process or to test its capabilities for full use in the future.

What TRIARE experts think about AI’s impact on software testing

As TRIARE experts, we assume that using AI in the QA process is quite a revolutionary and perspective option. You should try it to boost the testing process and automate workflow. When a team has a lot of tasks and all of them require urgent attention, having an assistant to take over some of the tasks is extremely crucial.  

We also believe that AI is useful when it comes to identifying potential issues that may arise in product running. It’s important to understand that this part of the work has to be done before the release, and AI tools are great at it. This significantly helps the company to reduce testing costs, as the process becomes more automated and less dependent on human labor.

So, we confidently recommend you use AI tools for testing as a reliable investment in the quality of your future project. This technology has already shown how revolutionary its solutions can be in development or even design. Our team will help you effectively implement AI in your testing process, which together demonstrates effectiveness, flexibility, and adaptability to any changes.

Conclusion

As you can see, AI technology has proven its effectiveness and has become an indispensable assistant for QA processes. Although it still needs to be controlled by testers, the fact that AI is already accelerating workflows gives us hope.

If you are currently facing the choice of whether to use AI to test your project, our experts will help you make the final decision. The TRIARE QA team will guide you through all the testing phases where AI will show its full potential, explain the benefits, and help you choose the right AI tool for your project.