How to Develop AI-Based Software Testing?

Almost every day new technologies are introduced in the world but not all of them have the power to transform industries and organizations all across the globe. Artificial Intelligence is widely utilized to understand and enhance the business. AI entering in every sector of life and has a huge impact on e-commerce, healthcare, cybersecurity, application development or software testing. Artificial Intelligence is the key to making the companies competitive and ready for the future.

According to Research, AI will take over all sectors of technology. Thus it will make its high position among the top investment priority CIOs this year, That means new software testing horizon and its scope will be wider than ever before. Today application interacts with people, other apps, devices, and networks as well. They do all these tasks at the same time.

Why AI needs in software Testing?

Through the help of AI developers and testers identify the issue, no matter how careful manual testers are, machines are more accurate. Since these tools are used to perform software testing and tester believes that manual testing gives better results. AI used to help in preventing task repetition and helps in speeding up the software testing process. Machines are used to test software applications and these processes take less time as compared to testing. That is why human testers do not have to do repetitive testing activities and even if there is a repetition, software testing tools can do all the work efficiently. 

Today most of the businesses trying to harness the potential of artificial intelligence in different ways. Artificial Intelligence could ease some major testings problems which include:

• Identifying the precise use cases
• Lack of awareness on what to do
• Based on input data, verifying the behavior of the applications.
• Checkings the app’s quality on various parameters such as stability, performance, and security that too in the least amount of time.

Developing AI-based testing 

Automating API tests generation and maintenance

Artificial Intelligence can be used to develop a set of complete automated test scenarios using manual UI tests. Developers can logic to understand the basic patterns and relationships in the different API calls that are made while executing the user interface. Once the behavior is understood, the next step would be to create a series of API calls in the user interface phase that represent the underlying interface calls. Artificial Intelligence plays an important role in researching and understanding the entire process around API resources. Upon learning and storing the test in a proper data structure will help. The aim of AI is to create more advanced tests and not simply automate existing ones. Para soft’s smart API test generator is one such tool that goes beyond records and playback testing, using AI technology to identify the pattern within the traffic, Building a comprehensive data model of observed parameters along with generating automated API tests that can learn and upgrade themselves.

Self-Heal Execution of selenium tests

The AI-based tool is used can be made for learning internal data during routine selenium tests such as detailed web UI content information, an attribute of DOM elements, locators, etc. The AI platform becomes smarter and saves a lot of time in observation if you understand and know through the historical study of these tests. Para soft’s tool selenic is a classic execution of this definition.

Automating unit test generation and parameterization

The gaps in testing can create a lot of problems for JAVA developers, especially when starting from a sparse JUnit Harness. On the other hand, AI-based software testing tools, along with automated test case creation, can include static analysis, unit testing, coverage, and testability. JAVA application can be tested with added automation in one click. 

Defect Analytics

Artificial Intelligence can be used is in defect analytics. Cigniti’s Intelligence test case management strategy includes an algorithm for Defect analytics. This makes sure that every error causing a defect is caught at its source. AI-based algorithms can quickly learn from past trends and go through the standard process evaluating defects in the least amount of time. 

Conclusion

Artificial Intelligence and Machine Learning can have more than just a few software testing applications. AI helps developers in identifying the errors that can be used to improve the code. Artificial Intelligence occupies every domain of our lives. AI changes software testing for good, by improving quality software development efficiency and processes. Nowadays few companies implement AI technologies for product development, There is no doubt that AI is here to stay and develop early.

Also Read: Artificial Intelligence trends in 2020

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