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Artificial intelligence (AI) is rapidly transforming many aspects of our lives, from how we work and communicate to how we access information and make decisions. It has the potential to revolutionize industries and bring about significant changes in the way we live and interact with the world around us.

One of the most significant impacts of AI is in the field of automation. Many tasks that were previously done by humans can now be performed by machines, thanks to advances in machine learning and natural language processing. This has led to the development of self-driving cars, drones, and robots that can handle a variety of tasks, including manufacturing, transportation, and even healthcare.

AI is also changing the way we communicate and interact with each other. Virtual assistants like Siri and Alexa use AI to understand and respond to human voice commands, while messaging apps like Facebook Messenger and WhatsApp use chatbots to provide customer support and handle routine tasks. These technologies make it easier and faster for people to get the information and assistance they need, improving efficiency and productivity.

In the business world, AI is being used to improve decision-making and optimize operations. Companies can use machine learning algorithms to analyze data and identify patterns that can help them make better decisions and improve their bottom line. For example, AI can be used to analyze customer data to identify trends and preferences, or to optimize supply chain management and improve production processes.

Another area where AI is making a big impact is in healthcare. Machine learning algorithms can be trained to analyze medical data and help doctors make more accurate diagnoses. AI-powered systems can also be used to monitor patient health and alert healthcare providers to any potential problems.

Overall, it’s clear that AI is changing the world in many ways, and the impact is likely to only grow as the technology continues to evolve. While there are certainly concerns about the potential negative consequences of AI, it’s clear that this technology has the potential to bring about significant benefits and improvements in many areas of our lives. So, we can say that the future is going to be very exciting with the adoption of AI in all sectors.

Today, software and business applications have become more complex and require the best quality and detailed testing prior to their release, deployment, and go-live. Applications need seamless testing and positive test results for business owners to make the proper decision for production deployment.  But a simple lack in the test script update according to the latest update in the application may significantly affect the general automated test flow. While Artificial Intelligence (AI) is a relatively new concept, it can be immensely useful in the field of test automation and automatically create new test cases and perform the testing using test bots.

Impact of AI on Test Automation

Automated software testing is a must in today’s time especially when speed to market and great end user experience is key to success. Everyone is aware of the importance of testing for applications. But now the question is what has AI got to offer in this aspect?  It can change the approach of testing and how it is implemented. The various potential solutions that AI can offer for the testing ecosystem are:

• Reduce Release cycle times
• Move from manually creating test cases to the automated way of doing things
• Adopt AI, Data, Algorithms, and newer techniques to advance the way you test.

Primarily the use of AI emphasizes test management and the concept of creating test cases automatically. It lessens the level of effort (LOE), with the built-in standards and keeps the process consistent. The second helpful use of AI emphasizes generating test code automatically by understanding the user requirements and acceptance criteria.

AI technology has yet another use for test community that is codeless automation, it will create and run tests routinely for various mobile and web applications without the automation guys writing any code.

How it works?

• AI is trained to understand your applications
• Tests are composed visually
• Functional tests are reliably executed

Training the AI Bots

Simply put AI is the capability of a machine to comprehend the environment and develop the input data to perform a smart action and then work to make it better automatically. Like the Voice search concept, Google Assistant and Amazon Alexa employ AI to process the input and do the smart actions to give the desired answers to users. Here is a lesson/example: the developers allow the bugs, and often they react in place of preventing. To develop AI test bots, developers must guide the bots to process the input data by asking the queries in order to make it perform smartly, same as Google Assistant. The bots will keep on improving constantly with the improvement of the algorithms to identify the input patterns and user behaviors.

How does a Bot learn to Test?

• What does an application look like?
• What is the business flow we are testing?
• The sequence of steps involved in testing
• What are the variables we need to consider in testing?

Purpose of AI tool implementation

The purpose of AI tool deployment is not for replacing manual testing, the goal is to explore the possibility of AI and machine learning use to improve the testing and receiving reliable results within the specified time.

Available AI tools

There are many tools being introduced in the marketplace to cope with the changing requirements of AI in the test automation industry. Few of those are listed below:

• Appdiff (test.ai)
• Applitools
• Mabl
• Sauce Labs
• Testim
• Others………..

Limitations of AI-powered tools

AI test automation is still in developing phase and has areas to be worked on. The hurdles you might face while creating AI-powered apps for testing are:

• Finding and making all the required algorithms accurate
• Gathering lots of data to feed the bots
• How bots response to the fed or input data
• The process of input of the data will be unending as the algorithms will keep upgrading.

Conclusion

To sum up, AI-based test automation is a great development from the testing aspect and can reduce a lot of effort on the tester’s part. They reduce cycle time and improve the end user experience in the least possible time for any web or mobile apps.

At TechArcis, our experts are trained to provide reduced time to market and great end-user experience with advanced Automation. Our testing professionals provide continuous testing to provide the desired outcome. We can be reached at contactus@techarcis.com

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