It might be unnoticeable at first, but machine learning revolutionized our world. Do you have a smart system in your home or office? Do you use Siri or Alexa on your mobile phones? Are you impressed with self-driving vehicles? We can thank machine learning and artificial intelligence for these breakthroughs. ML also changed many industries from the perspective of professionals, especially software developers. It plays a huge role in automating software tests.
Thanks to test automation, developers can finish the app in time while running repeated tests without manual involvement. Automated testing process also reduces the risk of bugs as it runs with prewritten scripts and can be used an unlimited number of times. Compared to manual testing, which requires human participation and the testing process takes a long time, automated testing can run the same test on several devices at the same time and increase testing coverage.
This guide focuses on the role that machine learning has in software testing. Here’s why ML and AI are imperative in this field!
What Is Machine Learning?
The simplest definition is that it’s a system of complex algorithms that allows a machine to learn by experience, which is also how people learn things. Thanks to optimal programming, the machine learns things automatically and adapts to user needs.
An example of machine learning is a self-driving car. As the developers test it in various simulations, the vehicle “learns” to perform better. The more tests pass the better product performance. The computer collects new data and processes it to achieve that goal. But if you look at it from the outside, it seems like the machine “learned” different things in the process.
How Can Machine Learning Help Software Testing
Tests are nothing else than “trial runs” for a product. If you are building a chair, you need to confirm each leg has the same length. Otherwise, you risk the chair being wobbly and the user falling from it. It’s the same with software development – testing is necessary to avoid minor issues affecting the entire product.
Thanks to testing, creators can identify bugs in early development phases. Manual tests used to be the only option, but they took a lot of time. It also meant that at least one staff member should handle testing instead of focusing more workforce on the development.
That’s where test automation and test learning transformed this field. First, automating the testing process meant saving time and resources invested in manual tests. The computer could now perform repetitive tasks with a clear determinant and do these tests 24/7, even when the employees aren’t around.
Benefits of Machine Learning for Software Testing
Why has automated testing with machine learning technology become a new trend? Here are the main benefits that ML secures:
- A comprehensive test analysis. The technology uses computer vision and other techniques. That enables testing a bigger part of the user interface than traditional test codes.
- Easier and smoother test creation. Thanks to this, you can create tests quickly and analyze large chunks of data efficiently.
- It works even outside the office hours. Automated tests only require a computer most of the time. Thanks to that, you can stay on track with the planned development schedule.
- A machine can pick details that human eyes can’t. For example, a visual interface might have buttons that are out of position for a few pixels. It’s almost impossible to see it with the human eye, but the machine can identify the bug.
- It delivers reliable and consistent results. There’s always a human error risk with a manual tester. But if you programmed the test correctly, the computer will consistently deliver accurate and consistent results.
Is Artificial Intelligence the Same As Machine Learning?
Although you’ll see people using these terms interchangeably, that’s not entirely accurate. Artificial intelligence refers to any situation where a machine uses intelligence to handle an issue. Machine learning is how the computer can achieve that goal, and it’s often the foundation of AI systems
Apart from machine learning, AI has other forms, including computer vision, natural language processing, and deep learning. The last is the most advanced option that uses deep neural networks to resolve issues without human involvement. The DeepMind Ai created by Google is an excellent example. It played the Go game against itself enough time to improve to the point where it has become almost unbeatable for a human.
How AI Can Help Software Testing
Artificial intelligence can help every phase of software testing, and here is how:
- Test creation. It’s impressive how Selenium and other frameworks replicate real user action during UI interaction. But it’s not easy to get the scripts right, and AI simplifies the test creation process. Apart from making it easier, it also saves a lot of time.
- Test analysis. Automated tests require a clear determinant that defines if the test is a failure or a success. Thanks to AI and visual testing, it’s possible to analyze how a website or app looks compared to the previous test. If there’s any change, the machine can flag it. AI can help you test more of the UI automatically, making it an important asset.
- Test maintenance. Adjusting tests can be complicated for engineers, but it’s necessary. For example, let’s say you moved the “Buy Now” button to a lower section on the webpage and switched its title to “Purchase.” Thanks to processes like self-healing, AI can still detect the button. This is only an example, and AI is capable of simplifying the entire test editing process.
Automated testing has already become a massive breakthrough in the testing industry. Thanks to machine learning and artificial intelligence, the future of these tools is even more exciting. It’s already exciting what ML and AI can do in test automation and how they make life easier for the developers.
Don’t hesitate to find an automated testing tool that utilizes machine learning to see why it plays a revolutionary role in the software testing process!