Quality assurance (QA) is an integral part of any industry aiming to deliver customers high-quality products or services. With the constant technological evolution, artificial intelligence (AI) has emerged as a powerful tool to enhance and streamline QA processes.

For organizations with prior experience in software development, it is essential to recognize the significance of quality assurance. Before AI’s advent, software QA employed a range of manual methodologies that necessitated continuous testing for consistency. This process consumed significant time and resources for businesses.

In this blog, we’ll explore the role of AI in QA, how it is used, and the steps to implement AI effectively with us as your outsourcing partner.

 

How Is Artificial Intelligence Used in Quality Assurance?

Artificial intelligence (AI) can revolutionize quality assurance (QA) by driving powerful and intelligent automation, facilitating a rapid testing process. As AI becomes increasingly pervasive, it simplifies the creation, execution, and analysis of software for testers, eliminating the need for constant manual updates. This is particularly crucial in AI projects, which demand continuous learning and management.

Moreover, AI testing tools can enhance the execution of tests that incorporate AI-powered visual verifications, yielding a wide range of outcomes. With AI’s advancements, the possibilities for efficient and effective software testing are boundless.

 

How to Use AI in QA

To get the most out of AI in QA, there are several steps organizations must take. Here’s a brief overview of the most essential ones:

 

Step 1: Pilot and Data Annotation

During this crucial step, the quality assurance (QA) team carefully selects the tests they will perform and establishes their desired outcomes. Once the scope and objectives of the testing are defined, the team proceeds to gather the necessary data.

The QA team meticulously chooses their testing methods to ensure the highest quality of training data for the project’s goals, AI models, and algorithms. This meticulous selection guarantees that the data utilized for model training is exceptional.

 

Step 2: Testing, Validation, and Scaling

To progress, the QA teams should create consistent testing algorithms and choose subsets of the training data to validate the tests. Next, they should apply the training data to a selected scenario to evaluate the performance of the algorithms, observe data behavior, and ensure accurate results from the AI model.

 

Step 3: Retrain

Periodic updates are necessary for each AI model. Once you have tested and confirmed the satisfactory performance of your AI model in quality assurance tests, the next step is to either retrain the machine learning model or continuously refine it based on the latest features and data.

The objective here is to ensure that your QA AI model remains up-to-date, consistently delivering high-quality results and creating opportunities for enhancing its accuracy. This process is commonly referred to as machine learning operations.

By leveraging machine learning to an expert team, quality assurance testers can focus on what truly matters and enhance the overall application quality. Ensure that your QAs are accurate by partnering with a company that has the expertise and the resources to do it.

 

Implement AI with Novacomp

Nearshore Outsourcing QA automation services offers businesses a strategic advantage in delivering high-quality software products to market faster and more cost-effectively. Also, it allows for more streamlined communication, and increased productivity to help your company focus efforts and resources on other types of testing.

At Novacomp, through our Staff Augmentation Services, we nearshore your IT Team and ensure training and compliance with agile methodologies. Our QA Testing consultants have knowledge and expertise in handling a variety of programming languages and employing automated testing tools that can help you save time and money. We can also help you develop a team of experts to handle your QA needs while working alongside our consultants on the subject.

Remember, a well-implemented AI strategy in QA is not just about reducing workload but also a key driver for consistent quality and innovation. Contact us today to help you ensure the ultimate performance of your software products.