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How AI can change Software Testing?

Shreya Bose, Technical Content Writer at BrowserStack -

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Artificial Intelligence (AI) and Machine Learning( ML) have graduated from becoming mere technical concepts and tools to full-fledged buzzwords. They have inspired everything from great films to philosophical debates. More importantly, they have opened up the possibility for incredible advancement in every possible human activity, be it medicine or gaming.

In such an atmosphere, it is important to discuss the effect of AI in the realm of software testing, automated testing in particular. What role can AI play in Functional test automation?

Let’s find out.

The Challenge

It is common for QAs to spend significant time on ensuring that new code does not break the existing codebase. Each time developers add new code, new tests must be carried out. Regression testing cycles are extremely time-consuming when undertaken manually and can burden QAs to a large extent.

Traditional QA methods involve checking off a list of tasks that ensure the software is working the way it is meant to. In the beginning, when only a few features have to be tested, this is a feasible task. But as features increase, QAs face difficulty in meeting deadlines while also performing comprehensive tests. The more complex an application, the more difficult it is to make sure it receives full test coverage.

Automated Testing has gone a long way in assuaging the challenges of manual testing. Repetitive tests such as regression and sanity tests can be automated via multiple tools, most notable of them being Selenium WebDriver.

However, with the advent of AI, the possibility of streamlining the testing process has magnified. Given that, due to competition, new software and product launches must be accelerated since extended deadlines are not really an option. Thus, there is no option but to test smarter rather than harder.

How AI can optimize Testing?

#1: Accelerating Timelines

Instead of having to go through thousands of lines of code, as manual testers have to do, AI can quickly sort through log files, scan code in seconds and detect errors in much lesser time. Additionally, AI does not get exhausted or make human errors, thus yielding more accurate results. By incorporating AI into repetitive tests, QA engineers can focus on testing new features or pay particular attention to important parts of the software.

#2: Better Automation

As mentioned above, a large part of a QA’s job is to ensure that new code does not destabilize existing functional code. As more features are developed and added, the amount of code to be tested expands and can overwhelm already burdened QA engineers.

AI bots, on the other hand, will be able to evolve with change in the code. They can adapt and learn to identify new functions. When AI bots identify a modification to the code, they can be programmed to decide if it is a new feature or some defect emerging from changes to the code.

It is also possible to improve automated testing by utilizing a platform that is built for it. BrowserStack, for example, provides a robust, 24/7 available Selenium grid of 2000+ real browsers and devices for testing. By leveraging such a grid, QAs can accelerate testing, thanks to features like parallel testing. Additionally, by testing in real user conditions, QAs can ensure that the website or app is free of all possible bugs that might tarnish user experience.

#3: More clarity on client requirements

In the realm of software testing, being able to understand what the client wants is of paramount importance. By using AI, it is possible to examine similar websites and apps to determine what contributes to success among the target audience. AI can help study a large number of competing products to identify key selling points so that both developers and tester know what users want out of a particular kind of software.

Testers can use AI to detect common flaws in code that might hinder the flawless functioning of a software system. Additionally, by achieving a proper understanding of what the client wants, they can create test cases to ensure that the product does not break when it comes to achieving those particular goals.

It is important to remember that incorporating AI into testing processes is still a process in motion. But it is also important to keep an eye on innovations and advancements in this regard.

AI will certainly help make testers’ lives easier by introducing greater accuracy and speed. By detecting bugs quicker and faster, AI can provide testers with the time and mental energy to create better testing methods, write better test scripts and devise ways to craft the best possible user experience.

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