Role of artificial intelligence in testing

Software testing is an imperative process that safeguards the application against potential dangers. Under a planned atmosphere, an application is being accessed and analyzed to comprehend the possible risks.

For the successful launch of new software, it is inevitable to do testing in a smarter way. It is quite evident that the key to making software testing efficient lies with Artificial Intelligence (AI).

By using technology, which can meticulously work like human beings, testers can go beyond the traditional mode of manual testing and progress towards a precision-based testing system.

Interestingly, the AI testing framework can recognize pitfalls more efficiently and with constant updates to the algorithms, it is feasible to detect even the smallest error. AI testing framework needs little maintenance and can explore new paths on its own. With companies waking to artificial intelligence, testers can use it to simplify the decision-making process and improve efficiency in the testing field.

Here we have listed below some of the salient benefits of artificial intelligence in testing.

To err is human. Even the expert tester can make mistakes while doing monotonous manual testing work. This is where AI in software testing can help by performing repetitive tasks with accuracy. When AI handles repetitive tasks, testers have ample time to work on new software and deal with sophisticated features.

It is daunting for a software tester to execute a web application test with hundreds of users. With the help of artificial intelligence, it is possible to interact with numerous web-based applications, and thus, one can go beyond the manual human testing limitation. 

The AI testing framework helps you increase the scope and depth of tests impeccably. It can check file content, data and is quick to detect whether the program has worked accordingly or not. AI in software testing allows the execution of over a thousand test cases through a single test run, which otherwise not possible in case of manual testing.

Due to automated software testing, it is feasible to detect errors in a short span of time, and as a result, software developers get quick feedback on the applications.

As an AI can work 365 days a year, it is possible to execute tests as often as required. The good thing is that it can take place in real-time with a high probability of correctness. With AI in QA, it is possible to analyse results and communicate the same to stakeholders quickly. As errors can be assessed by checking codes, the entire process becomes trustworthy. The result is—customers are happy, and the company generates more profits.

With every software testing, there is a modification in the source code. Manually repeating those tests can prove to be a time consuming task. As against this, once created, automated tests can be executed multiple times at a fast pace with no additional cost. With AI in testing, the testing timespan gets reduced from days to hours, which translates into huge savings. 

As the entire automation process is done on an integrated and embedded platform, it becomes easy for software developers to do execution on the client’s website. Hence the execution process becomes smooth.

Here are the five early contenders for Artificial Intelligence and machine-assisted automation testing tools-

Applitools Eye

It is an automated AI testing platform with targeted at automation engineers, DevOps and from end developers who want to make sure their applications like mobile, web and native apps are functioning correctly as per their requirements.

AutonomIQ

It is a platform that is used to automate the entire testing life cycle of any application from test case creation to impact analysis. It helps testing team to speed up their test cases, data and scripts creation.

Functionize

functionize

It is a cloud testing platform that accelerates test creation and executes thousands of tests in minutes.  It also enables automatic test maintenance.

Mabl

Mabl

It is a machine learning driven test automation for web applications that simplifies the creation of automated tests.  It also helps to identify regressions tests and automatically maintain the tests.

Parasoft SOAtest

Parasoft SOAtest

API testing is no more a new product.  However, the latest releases introduce AI to convert manual UI tests into automated, script less API tests.

Also Read : How Artificial Intelligence Can Help Software Testing

Conclusion

The prime objective of integrating AI in software testing is to create an environment where the software will be able to self- test and diagnose issues if any. By leveraging AI, organisations can test smarter and thus, pave their focus on the thing that matters most— software development.

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