Software testing is an integral part of the development process for every mobile and web application. However, traditional testing methods are often time-consuming, inefficient, and expensive.
Artificial Intelligence is changing the software testing landscape, ensuring testers can make arduous tasks more cost-effective and time-efficient. Certain manual tasks would lead to more human errors and delay an application’s release by weeks. Thanks to AI and Machine Learning, the testing process has been automated which simplifies the process of testing for specialists. Including automation testing strategy in the testing process allows companies to direct resources to more efficient practices and produce a more bug-free application in the process.
While Artificial Intelligence is not replacing every task just yet, it is becoming more prevalent in the testing process. This is the reason a lot of fresh graduates choose AI as a part of curriculum or some learn the skill through enrolling in some Artificial Intelligence Certification, Future industries are going to be completely dependent on AI. As a result, many companies are trying to automate as many testing protocols as possible.
Today, we’ll look at how AI improves the software testing process. Read on for more information.
Benefits of Using Artificial Intelligence in Test Automation
Artificial Intelligence benefits the software testing process in numerous ways. These include:
Finding suitable testers for the company
Many companies struggle to find suitable team members with the right skill sets for their testing requirements. Now, businesses can leverage AI-based testing strategies so testers can enjoy a more scriptless testing environment that does not require an intricate knowledge of coding.
The amount of time spent on avoidable, repeated tasks
Whenever a new application testing project comes about, testers need to create similar codes to ones they have already used in the past, which can take days. Artificial intelligence can help generate these test scripts automatically. AI tools can be taught based on inputs from other projects to generate test scripts, saving more time.
Understanding failed tests
Traditionally, testers could spend hours figuring out whether a failed test was caused by a broken test case or bugs within the application. These tests are referred to as flaky tests and can delay an application’s release needlessly—the flakier the tests, the slower the software delivery.
Artificial Intelligence can help teams avoid flaky tests by creating more accurate test cases for the project and picking up on trends in test failures, making the bug identification process much quicker.
User interface changes need frequent script modifications
Every business will go back to the drawing board with their user interface to ensure the app provides the best experience for users. However, even if they only make a small change, it could cause test script failures when executing functions on the page.
AI and ML-based technologies can be trained to find changes in code or other application issues; the technology then makes the right steps to fix those problems, reducing the need for human script updates for small UI modifications.
Maintaining the test scripts and test suites
Manual testers finger maintaining many test scripts becomes difficult as the application grows. However, AI tools can maintain and analyze test scripts based on your testing requirements. In short, AI testing overcomes time-consuming practices and makes these types of processes much more efficient.
Why Do We Need AI in Test Automation?
So, why do we require artificial intelligence for test automation? There are numerous reasons:
- We need a tool for testing systems that can intelligently recognize particular regions, trends, and issues within this application’s coding, so testers do not need to perform repetitive tasks to fix issues and bugs manually.
- Software testing requires a huge investment in time, resources, and capital. As developers look for quicker ways to release apps with a currently inadequate testing strategy, AI presents the best way forward.
- Currently, 80% of testing involves repetitively checking software functions and protocols. Artificial Intelligence can make this process much more efficient instead of relying on a human tester to perform those repeated tests. In addition, by leaving repetitive work to the Artificial Intelligence-based automation, testers can dedicate more time and effort to the remaining 20% of testing operations.
- Artificial intelligence and human intelligence can be leveraged to recognize complex application issues through innovative test environment generation. As such, a hybrid testing strategy between automated and manual testing could be beneficial.
- Artificial Intelligence algorithms can be helpful when making a more product and beneficial software for the end-user. That being said, testers and developers still need to understand how to use Artificial Intelligence creatively.
- An intelligent algorithm can help testers find more bugs in less time and achieve maximum coverage when testing any application. Afterward, developers can use those testing outcomes to develop the product further, learning from a trial and error strategy. This would take much longer without AI.
As you can see, AI makes the lives of testers and developers so much easier. When testers have a more efficient trading strategy, they can focus their efforts on more creative testing solutions, and companies won’t need to wait as long for a fully-functioning, bug-free application.