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AI Can Help with Secure Quality Assurance Testing


Artificial intelligence technology has become instrumental to the research and development process. In May, the White House actually unveiled a proposal to increase investments in research and development for AI projects.

There are many ways that AI can help with the development and release of new products and services. Most of the discussion focuses on using AI to come up with the initial idea and automate the research process. However, companies can use AI for an equally important initiative – quality assurance. We talked about the benefits of data analytics for QA teams, but AI can be just as important.

Rajneesh Malviya, the Vice President and Global Head for Infosys Validation Solutions, has talked about the many benefits of using AI to improve the quality assurance process. Your company should consider these benefits.

AI is Crucial for Handling the QA Process When Developing New Products

The process of ensuring that your product or software is of the best quality for your clients is referred to as quality assurance testing or QA testing. QA refers to the processes that are carried out in order to prevent issues with a software product or service. These issues could appear as the result of an incorrect implementation of the features or access controls. Hence, teams ensure that users have the best quality product or software. AI has proven to be very useful in this process, as Malviya discussed.

This process can become very complex without the right protocols in place. Fortunately, AI technology can help immensely with QA professionals trying to make sure that they bring the best products to the market.

Performing Quality Assurance Testing with a Security Approach

There are many ways to use AI technology to make quality assurance testing more effective, such as combining manual and automated methods for performing QA testing or developing test cases that closely match the application’s requirements.

AI technology has helped automate many of the QA processes. Companies are using RPA & Robotics solutions to streamline these processes more easily.

AI Can Improve Manual Testing in Addition to Automated Testing

AI technology isn’t just useful for automating elements of the testing process. They can also help companies improve the manual testing process in various ways.

One of the biggest benefits of AI for manual testing during the QA process is that it helps companies better train their employees. Companies can use machine learning tools to identify some of the most common product defects that would otherwise be overlooked. Employees can use these insights to examine products more carefully.

When conducting quality assurance testing, both the manual and automated approaches should be applied. When it comes to manual testing a lot of teams use the shift left approach while developing and testing software, especially in the early stages of development.

Testing manually gives you the ability to cover a wide variety of test cases with proper logic. Since a user fundamentally interacts with the look, feel, and UX of the product during manual testing, this methodology is seen as being vital. Manual testing is typically utilized for exploratory testing, testing for usability, and adhoc testing. AI may not be able to replace these aspects of the manual testing process, but they can certainly help streamline it.

On the other hand, there are situations when a couple of tests need to be executed repeatedly. In such cases, an automated technique has to be followed. It will execute the tests successfully and will save a significant amount of time and resources.

AI Can Help with Risk Scoring During the Testing Process

The primary objective of the risk-based testing technique is to identify the problems that pose the greatest risk during the testing process. Testing of this kind gives quality assurance teams the ability to prioritize and properly focus their efforts on the most severe risks that could result in issues with the application’s performance.

When a risk-based approach is taken, the team’s primary focus is on problems that could occur when the product is being used, such as adding negative values of a product, which could lead to an error and could cause the software to crash if the error is not handled properly. Therefore, using a risk-based approach improves the overall user experience and doesn’t require that much maintenance.

AI technology can be very helpful in this regard. One of the biggest benefits of using predictive analytics tools is that they can anticipate the likelihood of various problems arising with a given product. They have clear risk scoring algorithms that can significantly improve the QA testing process.

Using a Multi-Directional Testing Approach

It is always recommended to have multiple categories or steps of quality assurance testing. The product’s development team should be required to create automatic unit tests so that they can evaluate the quality of their code at the beginning of the development process. This will allow them to easily fix any problems that may arise in the initial stages, saving a significant amount of time. When everything is set, the quality assurance team will run smoke tests to ensure that everything is ready for implementation in production.

When you begin employing the multi-directional testing strategy in software development, it immediately indicates that a great deal of testing in a variety of directions will be carried out in depth. This type of multi-directional testing will ensure that all of the bugs have been fixed. After enormous changes have been made and the release criteria have been met, the QA team will ensure that all the functionalities are working seamlessly.

AI is Vital to the QA Process

QA testing has to be conducted to validate that the product we are providing meets the users’ needs and also delivers a higher user experience to them. Fortunately, new advances in AI technology have made it easier than ever.

More companies are using AI technology for different reasons. One of the most important benefits is that it will make it easier for the developers to fix any errors that may have been found during the initial phase or during the testing process. It will also ensure that the final product is of a high quality and will not result in any problems for the end users. As a result, QA needs to be carried out in a comprehensive manner and using the appropriate procedures, all of which are covered in this article.


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