Person looking at complex algorithms | Mammoth-AI | Testing Center of Excellence

When it comes to software development, test automation, such as quality assurance (QA) tests, is imperative. As a software developer, you will typically need to do more than just write codes to develop an application because you will also need to conduct testing and see the impact of codes on those test cases. In doing so, developers can consistently deliver quality products, and many businesses can benefit from various applications.

The good thing is that use of Artificial Intelligence (AI) and machine learning (ML) has been introduced for the advantage of businesses. Their emergence greatly helps in forecasting, augmenting, and accelerating test automation to ensure continuous integration and quality application. 

In this article, we will share with you three key steps for your QA test when utilizing AI and ML:

1. Check for patterns during QA testing

When it comes to continuous testing and deployment cycle, collecting data is paramount to the process. These valuable data and information are essential to detect errors and technical glitches and see how stable and solid the application is. 

What’s good about the integration of AI and ML is how they can recognize patterns formed within test automation cycles. These patterns are essential to examine specifications change so that programmers can implement those specs, which is why it’s necessary to check for patterns during QA testing.

2. Set data predictability for seamless integration

As far as test automation is concerned, predictability is the key to a seamless integration. It helps if developers can predict which parts of an app are verified accurately by a set of tests. It’s all the more required in an environment where regular testing is a part of a constant delivery pipeline.

What’s great about AI and ML is how they can set a predictable level of test coverage for an application. In fact, you can use test data generation tools integrated with AI and ML to predict the minimum subset of classes you need to test to achieve branch coverage. They can greatly assist developers in the testing process.

3. Rely on human intervention for complex algorithms

While you may strive to rise above manual tasks and tedious human processes, there are instances when human intervention is required, especially for complex algorithms. While AI and ML have significantly impacted how testing and business are conducted, expert assistance is still sought after so that business can have seamless processes. 

When faced with complicated situations, you can combine the power of technology with the aid of human intelligence to solve any problem.

Conclusion

As you can see, test automation, such as the QA test, is an integral part of the development of an application. The utilization of AI and ML can significantly impact the process and efficacy of test automation. Yet, make sure to check for patterns, set data predictability, and seek human intervention for complex algorithms, as outlined above. All these can make your QA testing more robust and take your software or app development up a notch!

We provide real-world business solutions and services using AI. If you need help with your Quality Assurance Test for your business, get in touch with us today to see how we can help!

Leave a Reply

Your email address will not be published. Required fields are marked *