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Why Enterprises Must Shift from Continuous Testing to Autonomous AI-driven Test Automation

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As the manifestation of human judgment, artificial intelligence can simplify the continuous testing process by eradicating manual interference. With artificial intelligence, testing teams can leverage automated test cycles, in which errors are recognized and corrective measures are taken in run time itself.

This is done without the need for human assistance and is completely based on insights gathered from previous data sets and past events. In this method, the artificial intelligence algorithm will guarantee that only an accurate code proceeds from one stage to the subsequent, organizing quality across the entire software development lifecycle.

Why is AI-driven Test Autonomous important

Ever since the emergence of the COVID-19 pandemic, application or software releases are taking place continuously on a daily basis.

Enterprises are rolling out new updates to keep up with the ever-changing consumer demands. Hence it has become imperative that the software has no errors and meets the time to market requirements. applications or software with error will affect the business continuity as app delivery will be delayed and maintenance will significantly increase expenses.

Therefore, now more than ever enterprises have to efficiently streamline software testing by making it smarter and simpler. 

Why should enterprises leverage AI-driven Test Automation?

AI-driven Test Automation is the process of running a program with the intent of finding errors. Increasing the quality of testing is an effective way.

Manual testing with the utilization of testers and automation testing by leveraging algorithms and computers are the two commonly used methods for testing software, applications, and products. Manual testing is also called static testing. It is carried out by the tester.

Automation testing is also called dynamic testing. As a result of this acceleration, it was necessary to accelerate the tests performed at different points and to run them more algorithmically. In the face of this necessity, software testing automation, as well as manual tests, have made a rapid entry into the market.

Manually testing in most cases is time-consuming, costly, and chaotic. Some of the main reasons for the development of AI-driven Test Automation are listed as follows. 

  • Higher Testing Efficiency 
  • Greater Accuracy and Reliability 
  • Reusability and Repeatability of Test Scripts 
  • Improved Test Coverage
  • Simulation of User Environment 
  • Higher ROI: Saves Time and Costs 
  • Volume and Simultaneity 
  • Early Detection of Bugs 

Applications of AI-driven Test Automation tests differ from manual tests unlike manual tests, automation tests are not suitable for all areas. The main uses of automation tests are regression tests, data-driven tests, smoke tests, static & repetitive tests, load and performance tests.

Automation testing is a good tool for testing functional and non-functional test types.

Testing an application with resources, tools, infrastructure, etc in the software development process is considered to be a high-cost factor. To minimize these costs and depend less on the manual tester, test automation has been considered as a useful alternative. Automation tests require manual test runs. On this issue, the following is that Instead of increasing human resources during the test, improving the level of test automation and risk reduction offers an approach to assist manual testing.

One of the most important issues for AI-driven Test Automation is to choose the automation testing tool and the appropriate framework. 

The importance of test redundancy in automation?

As a software system or application evolves with new updates or technology integration, its test suites must be updated frequently (maintained) to validate the current or modified functionality of the solution. These continuous updates made on the same test code may cause the code to erode; it may result in a large code that is totally complex and unmanageable. Hence, it will only increase the cost of test maintenance significantly.

Decayed parts or old sections in the test suite that are a significant reason to cause test maintenance issues are mentioned as test smells. Nowadays, with traditional testing, redundancy (among test cases) is often discussed but they seldom study test smell.

A redundant test case is considered significant because if one error is removed it will not at all affect the fault detection effectiveness of the entire test suite. 

The benefits of AI-driven Test Automation

Effectiveness and Precision. 

The main purpose of using AI-driven Test Automation is to reduce the number of test cases in a test suite. Decreasing the cost of software maintenance. Thus, if the testing turns out to be very time-consuming, then it will not be worthwhile to be applied.

Although the most reliable method to improve the efficiency of the process is to automate all necessary tasks, at this step it is not feasible to automate all of them. Thus, as we explain next, human intelligence is currently required in this process.

To complete a manual review on the test suite with the intent of identifying redundancy, examiners need to contribute time and energy to each test source code and connect them together. To minimize the number of needed effort, AI-based automation is the recommended procedure in a way to decrease the number of tests required to be examined (by using the suite redundancy metric).

AI-driven Test Automation Services suggest useful information such as pair redundancy metrics that helps testersfind other proper tests to compare with the test under inspection. This improvement was seen in several software solutions and it helped testers analyze both time and efforts more precisely with more than one insight.

Cost/benefit comparison

In addition to the efficiency of the process, the precision of redundancy detection was also evaluated. However, human error is as usual considered to be inevitable in collaborative processes between a group of people, this can significantly affect the precision of the whole testing process.

To eliminate this kind of error, the tester will have to be accustomed to the written tests. Therefore, AI-driven Test Automation enhances the detection process and identifies errors promptly. It reduces the size of the test suite by keeping the fault detection effectiveness of that. 

Increases the scalability 

Enterprises can significantly Increase the scalability of testing to a great extent with AI automation testing. QA teams can seamlessly separate the process of redundancy testing among multiple testers. These separate parts can be tested individually and integrated back together once the test cycle is completed. However, precise teamwork communication is required to make the whole process successful.

The flexible stopping point of the proposed process is another reason for its scalability. Based on the discretion of the test analyst, the method of redundancy detection may come to a halt after analyzing the subset of test cases or continue for existing tests. For instance, while testing large systems, by examining the expense for redundancy detection, the test lead, QA analyst, or project manager may choose to analyze only the significant part of the system.

Conclusion

For enterprises to deliver acceptable quality and reliability to their global customers. They must ensure that whatever the product might be, be it any sort of advanced embedded engineering product, that consists of several inputs parameters, or even software/hardware configurations, they must be thoroughly tested against for conformance. If the input combinations are large and the time to market demand is more, manual testing is next to impossible due to the requirement of efficient testers who can complete the entire test in a given time.

AI-driven Test Automation is the answer to precisely test all these case studies efficiently without involving a lot of manual intervention.

Author – Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services. He is also a contributor to several prominent online publishing platforms such as DZone, HubSpot, and Hackernoon.

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