We are living in a time of a big boom of new possible solutions in the tech world. Innovations are emerging at a rapid pace, bridging gaps and opening doors to fresh possibilities. The Quality Assurance field is not missing out either, as the digital landscape evolves. There’s a new trend where AI is influencing how QA professionals work, bringing a fresh perspective and flexible approach to their methods. The tools of QA are undergoing a significant transformation as well, moving beyond traditional methods to embrace predictive and automated strategies. The integration of Artificial Intelligence (AI) into Quality Assurance (QA) signifies a monumental shift in the software testing paradigm. One of the biggest upcoming promises of AI for QA is to enhance efficiency, and as a specialist, you might already know that this is generally wanted and highly appreciated. That is not the only promise given. 
Let’s look on few cases, the AI tools might help you with.

1. Predictive Analytics for Efficient Testing

Historically, QA professionals manually identified potential problem areas within software and later addressed problems thanks to automation. Today, a new option could come in handy. AI-driven predictive analytics can forecast where defects might arise, helping testers eliminate them before they become problems. By analyzing historical data and user behavior patterns, AI tools can prioritize test cases, ensuring that the most crucial segments of the application are tested first.

There are already existing software solutions that can help with this, like:

Eggplant: Known for its user-centric testing approach, Eggplant has become a popular choice for many organizations. Its ability to test any platform and any device has made it stand out.

Katalon Studio: This tool has gained significant traction in recent years due to its comprehensive set of features for web, API, mobile, and desktop app testing. Its user-friendly interface and integration capabilities have contributed to its popularity.

Tricentis Tosca: Recognized as a leader in the Continuous Testing space, Tricentis Tosca is renowned for its script-less approach to test automation and its extensive integration capabilities.. Automating Repetitive Tasks

Automation in QA is not new. However, the integration of AI takes it a step further. Tools equipped with AI capabilities can identify and rectify errors without human intervention. These tools can also learn from previous test cycles, refining their approach with each iteration. Familiar tools like Selenium are now being complemented by AI-driven counterparts, enhancing efficiency and accuracy.

2. Automating Repetitive Tasks with AI

While automation has been a part of QA for some time, AI introduces new options to save time and highlight innovative methods of automation. Thanks to continuous learning and memory, AI-empowered tools can identify and learn from known errors, making the testing process more efficient and accurate with each cycle. Moreover, saving time. Examples of tools that might help you with this are:

Testim.io: Its ability to autonomously create and execute tests using AI, with reduced maintenance over time, has made it popular among many organizations. Functionize: Its machine learning capabilities that allow for self-healing tests have made it stand out in the QA community.

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3. Bridging the Gap between Development and QA

AI’s role isn’t limited to just testing. Artificial Intelligence plays a multifaceted role in the software development lifecycle, reaching far beyond testing alone. One of the significant contributions AI provides is enhancing the collaboration between the design, development, and QA departments. By analyzing and presenting data related to user interactions, current market dynamics, and the overall performance metrics of software, AI ensures all teams align, guiding them toward a unified direction for the product’s development.

I bet many of you have come across Jira (by Atlassian). Primarily known as an issue and project tracking tool, Jira boasts plugins and integrations that leverage AI. These provide insights that assist teams in prioritizing tasks and understanding project health.

Another tool that can aid in this endeavor is CodeClimate. Widely recognized for its code quality metrics and insights, CodeClimate helps teams uphold a high standard of code, thereby promoting collaboration between the development and QA teams.

4 Improved Accuracy

Manual testing can be prone to human errors. AI-driven automated tests ensure consistent accuracy, as they can execute the same set of tasks without deviations or oversights. 

While manual testing has its advantages, it’s also prone to the natural limitations of human attention and fatigue. Simple oversights or unintentional inconsistencies can appear, especially during repetitive tasks. 

On the other hand, newly driven AI automated tests bring a level of precision and reliability to the table. These tests, once set up, can repetitively perform the same tasks, ensuring each step is executed with the same level of attention and detail every time. This consistency eliminates the room for errors that might arise from human lapses.

It’s important to note that this has already been successfully achieved thanks to automation processes, not AI automated tools. However, AI could offer a new perspective and ease, helping beginners or those looking to understand or save time.

The tools to consider are:

Appvance IQ (AIQ) as one of the pioneers in AI-driven testing, offers a unified test automation platform that uses AI to generate test cases, detect and validate data, and ensure applications are performing as expected. 

Applitools is known for its AI-powered visual testing and monitoring. This tool uses Visual AI to help detect visual regressions and inconsistencies across different browsers and screen sizes.

5. Help with visual Validation Testing

When you are developing a product , it is necessary to maintain an unified visual appearance across various devices, browsers, and screen sizes.

This uniformity is there, so the users have a seamless and same experience, whether on the web or mobile devices. Traditional testing methods often involve precise and long-lasting manual checks to verify visual consistency. However, with the advent of AI in testing, this process could be improved. 

AI algorithms are now equipped to automatically compare and validate the visual elements of an application across different environments. These algorithms can detect discrepancies, even subtle ones, in the UI/UX presentation. By doing so, AI could help provide a guarantee that the visual integrity of the application remains unified across all devices.

We already mentioned Applitools before, but Applitools is well-known for its AI-powered visual testing and monitoring capabilities too. It uses Visual AI to detect visual regressions and inconsistencies across different devices, browsers, and screen sizes.

Another game changer is Percy. It helps teams catch visual bugs and ensure that the visual appearance of web applications remains consistent. In summary, Percy works by capturing and comparing screenshots of your web application to detect visual changes automatically. Its AI-powered algorithms make it efficient in identifying visual regressions, and it integrates seamlessly into your development and testing workflows for continuous monitoring.

Conclusion

The integration of AI in QA is not just a trend — it could become your everyday tool to use. AI enhances efficiency, improves accuracy, and supports collaboration, ensuring a higher quality of software. As technology continues to advance, embracing AI in QA will be important for organizations aiming to deliver flawless digital experiences to their users. In this article, we have listed just a few potential ways how AI shapes the QA industry. There are many other ways to explore, and we will look at them in following articles.

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