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How Artificial Intelligence Will Make Its Mark In The Software Testing

Manchun kumar by Manchun kumar
January 8, 2018
in Artificial Intelligence
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How Artificial Intelligence Will Make Its Mark In The Software Testing
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Artificial Intelligence is not only changing our way of working, instead, it is also transforming a number of aspects of the software development world including testing. The AI technology is implemented within development teams and they can not only point out the errors or bugs but also can assess and correct them to ease the task of developers and testers.  Due to AI integration in the process of software development, the testing process has not been a compliance so far instead have become an essential and unavoidable step in the development process. This article discusses the effect of AI on software testing through following topics:

Why AI or Artificial Intelligence is Popular

Just like analytics people are interested in working with the intelligent systems. In analytics the decision is being taken on rapidly changing data. Here the patterns are being caught even if you do not know that how the data pattern will look like? Even incase of data deficiency the system can suggest you a vast quantity of data and provide you with recommendations about the next suggested steps and further refine the steps of your process.

So AI is so much popular in today’s market as it can radically improve the value of human resources, profitability and your marketing strategies if is being done in a right way.

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How is AI being used by software testing industry to improve business performance?

People are using AI for various tasks and in various steps of any software development program to make the complete process smarter and faster. For testing process the concept of AI can be implemented in three ways first is to implement an algorithm that can behave and process data just like a human.

The second way is to figure out the steps or processes where you can implement AI, here again a smart algorithm can help you to find out these processes. As a third way you can also use the system results to train and refine the system. These are a few ways to implement AI in the testing process, however you can also explore more areas and steps for implementation.

What are the benefits of AI implementation in software testing?

There are a number of steps involved in testing, however, so far AI has been implemented for only one key element of testing, which is test execution. AI and analytics together can deliver recommended tests which should be carried out to make the system learn continuously. This way the development team can fix the issues immediately, even sometimes before they occur.

How Artificial Intelligence Will Make Its Mark In The Software Testing 1

What is the role of asoftware tester in AI implementation?

A manual software tester still has a key role in the software testing process as AI is not a robot which can make your job easier. AI can be considered as a smart assistant, which can make the testing process easier. A software tester is still responsible to set up the testing environment and to model the workflows. Moreover, just like other testing cases the testers are responsible to review the testing results and to provide recommendations through various interfaces from the system. The feedback is also provided by the testers to the development team and business owners.

What should be the formalities that should be added to the workflows while adding AI?

While implementing any new technology is the lack of awareness of the new technique and the way to make it productive and effective. The biggest barrier in any of such implementation is the sociological barrier of human psychology of lack of adoption. To resolve such issues, you must try to experiment and embrace the projects which you can make more productive.

How can AI transform the way of developing and delivering software?

As AI is transforming the way of-of working and software development. AI algorithms can intelligently and smartly navigate the applications where the quality improvement is required the most. It can also identify the key areas where the developers should focus to improve the responsiveness and efficiency of the application. In this way the user can make his customers happy and help in retaining the customers for a long time.

Why is AI needed for software testing?

As the infrastructure is lacking and faster deployment is needed for software testing. The testing process involves three aspects including manual testing, user acceptance testing and the testing with the real world customer to know the overall performance of the software. It is believed that a completely tested software can be implemented successfully at client’s site.

As the manual testing cannot pace with the development cycle and let the user know about the improvement opportunities, so AI is used to implement the algorithms to discover the improvement opportunities through AI bots and through AI the same testing processes need not be repeated.

Final Words

Although testing is a manual process it also involves a number of steps which are to be performed repeatedly.AI can make the complete testing process faster and smarter. Today a number of software development firms are implementing, testing and other software development steps with AI. Complete SDLC process steps are automated and have become smarter after AI implementation in many processes of software development life cycle. Even the applications can be made bug free through the implementation of AI. Many companies are turning the process smarter due to such technological implementation.

Tags: software testing
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Manchun kumar

Manchun kumar

Manchun Pandit loves pursuing excellence through writing and have a passion for technology. he has successfully managed and run personal technology magazines and websites. he currently writes for JanBaskTraining.com, a global training company that provides e-learning and professional certification training. His work has been published on various sites related to Oracle DBA, Digital Marketing, Big Data Hadoop, Java, Dot net, SQL, Data Science, Salesforce, AWS, PMP, Devops, Business Intelligence, Project Management, Cloud Computing, IT, SAP, Project Management and more.

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