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Key benefits of AI in testing

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By John White Last Updated on Jun 14, 2021

Improved Accuracy to err is human.

Even the very careful tester is bound to happen mistakes while carrying out repetitive manual testing. This is where automated testing helps by performing the same steps accurately every time they are executed and never miss out on recording detailed results. Testers unregulated from monotonous manual tests have more time to develop new automated software tests and deal with cultured features.

Going Beyond the Limitations of Manual Testing

It is probable, not possible for the most important software/QA departments to implement a controlled web appl test with 1, 000+ users. With the help of automated testing, one can simulate tens, hundreds, or thousands of virtual users that can interact with a network, software or web applications.

Suggested Read: Adding exploratory testing for optimum QA coverage

Helps Both Developers and Testers

Common automated tests can be used by developers to clasp issues quickly before sending to QA. Tests are able to run automatically whenever source code, modifications are checked in and notify the team or the developer if they miss the mark. Functionalities like these save developers time and upsurge their confidence.

Increase in Overall Test Coverage

With the help of automated testing, one can upsurge the overall scope and depth of tests resulting in the overall enhancement of software quality. Automated software testing can look into memory and file contents, internal program states and data tables in order to determine if the software is behaving as it is expected to. Test automation is able to execute 1,000+ various test cases in every test run offering coverage that is not possible with manual tests.

Saved Time + Money = Faster Time to Market

With software tests being frequent every time source code is changed, manually recapping those tests can be not only time-taking but very costly. In disparity, once developed – automated tests can be performed again and again, with zero additional cost at a much quicker pace. Software testing timespan can be reduced from days to mere hours which translates directly into cost savings.

Read: Leveraging AI and automation for successful DevSecOps

Some of the popular AI based test automation tools being used are listed below.

Testim.io

testim

This tool creates the use of Machine Learning for the authoring, performing, and maintenance of automated tests. It highlights functional, end-to-end testing and UI testing. The tool becomes smarter with more runs and increases the stability of test suites. Testers are able to use JavaScript and HTML to write critical programming logic.

Appvance

appvance

Appvance allows the use of Artificial Intelligence to create test cases based on user behavior. The portfolio of tests comprehensively covers what actual end-users do on production systems, making it 100% user centric.

Test.ai

Test.ai is mobile test automation that uses AI to perform regression testing. It is helpful when it is about to get the performance metrics on your software and is more of a monitoring tool than a functional testing tool.

Functionize

Functionize needs MI for functional testing and is very like to other tools in the market regarding its abilities such as being able to develop tests quickly, perform multiple tests in minutes, and carry out in-depth analysis. With every passing day, as Artificial Intelligence finds its way into the software development lifecycle, organizations are still contemplating whether they should adopt it wholly within their product engineering practices. Post the initial investment to set up an AI system in test automation is carried out, organizations are bound to produce greater testing rewards for less money.

These savings must be sent towards QA efforts when it comes to testing exploratory testing or the more exciting & creative parts of software testing. Given the pace at which the industry is moving, it is safe to assume that the new age of software testing is well and truly here! Machine Learning and Artificial Intelligence are undeniably growing to be significant elements in software testing and Quality Assurance as well as paving the way for competitive positioning and better customer experience.