How AI-Based Automation Helps With the Quality Assurance Process

by Procurement Freelancers Team
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Artificial Intelligence has tremendous potential. The fact is, the concept of Artificial Intelligence or AI is required everywhere. You simply cannot ignore the role played by AI in areas of digital transformation of services. With AI concepts being integrated, maintaining the quality of products and services becomes a lot easier than ever. Quality Assurance is something much neglected during the early days of industrial digitization. However, things have changed a lot in the past few years. With the introduction of Artificial Intelligence, the way Quality Assurance used to work is going through some changes. A continuous testing process is necessary to ensure the best QA results. However, AI offers agility, and hence, the traditional testing procedures are taking a backseat. AI offers a lot of innovation and better automation when it comes to QA services.

The faster way towards quality testing through AI

The age-old process of software testing was a cost-intensive and time-consuming process. Then again, there stands the challenge of changing the needs of the clients by the time the testing process concludes. The entire effort on testing goes in vain. This is where the concept of AI comes as a savior for quality control experts. With the introduction of AI, experts are prioritizing the automation testing process. Compared to manual testing, the automation process is faster and a lot more error-free.

How AI helps with the QA process

AI-based automated software solutions assure superior results in the process of Quality Testing and Assurance. If observed properly, you will notice AI-based QA procedures benefits in three different ways:

  1. Eliminating chances of overlapping in context to test coverage areas
  2. Shifting base to defect prevention from defect detection
  3. Optimizing efforts with better predictable testing

Standing in this highly technology-driven era, most of the reputed organizations come with better machine learning algorithm setup for carrying out Pattern Analysis. It also helps in the fast processing of data in bigger volumes. As a result, organizations can take better run-time decisions.

A simple example to justify this fact: When the software is being upgraded, machine learning-based algorithms have the power to carry out code traversing for detecting key changes in the functional aspects of the software and then link them to the requirements for the identification of test cases.

The concept of robotics through AI has been proven to be worthy of different automation needs and also much effective compared to the traditional testing activities. Sourcing agents are considering this option strongly and hence opting for the robot testers to carry out testing on different physical devices like mobile, ATM, etc. The robots are developed using AI and can be controlled or programmed even from remote locations that allow less need for any co-location.

AI-based automation ensures better business results

The concept of the automation process in QA is not new. However, businesses often overlooked the numerous advantages associated with this process. Also, the concept of AI-based automation process was absent in the earlier days.

During the earlier days, the automation process was centered mostly on regression and based a lot on the user interface. During those days, the primary goal was to develop a framework capable of accelerating automation through different commercial tools. The evolution of automation witnessed processes based on data-driven, keyword-driven, and finally business process-driven frameworks, resulting in significant gains to the clients. However, the gains did not make much of a difference to the entire business approach and were mostly limited to regression, as per the opinions of quality control experts.

Then came the next stage of automation that targeted the functional sides of businesses using test data automation, middleware/API-based automation, and similar options. This approach introduced the concept of multi-tier automation and hence, experts start to witness the positive gains and on-time delivery of products.

It is this stage of automation that is getting updated to date. It is evolving further with a focus on continuous testing. Behavior-driven and test-driven designs are not limited only to the requirements of testers. Rather, they are emphasizing the integrated automation solutions for joining the mainstream.

The surprisingly fun fact: businesses and developers are eyeing strongly at the automated scripts for quality testing and hence, saving a lot of time. At the same time, it must be noted that the concept of testing is not limited to the black box. Rather, the focus is given also to internal perspectives for a better QC process. Sourcing agents are preferring automation processes during the execution phase of testing due to the availability of open source solutions, continuous and agile testing, and even 3rd party integration around mobile and digital testing.

Conclusion

AI is making quite a bit of noise. Integrating AI for quality assurance projects has proven better results. AI-based Quality testing is surely the new norm for businesses. We hope it keeps evolving with time.

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