AI can be a key to streamlining business software development projects by making testing smoother and more efficient.
What Does AI Mean for Software Test Automation?
First, it helps to understand that automation is not the same as AI. Automation can do things without human intervention, but it follows pre-programmed rules that a human developer must define. Automation is used for repetitive tasks that always follow the same pattern.
Artificial Intelligence, on the other hand, makes decisions based on patterns it finds in the data without being explicitly programmed. It can learn from past experiences and change its process in order to self-correct. AI essentially is a technology that attempts to simulate human intelligence and decision-making.
While AI is not the same as automation, AI can be used with automation to make decisions in tasks that have been automated, like in software test automation. Here are some ways developers and QA engineers can use AI to make automated testing smarter:
- It can automatically validate a user interface, so UI tests learn when it has changed and can do visual comparisons in the interface which returns whether it matches or not, soon adding the ability to put a percentage tolerance.
- It can automatically generate API tests based on their definitions and update the tests when APIs change.
- It can assess code changes and reveal the places in the code base they affect so that an entire suite of tests doesn’t have to be run with each change.
- It can identify tables and grids, extract the data, and verify its validity.
7 Advantages to AI-based Testing
Software test automation has changed the way QA testers do their job, and now that it is mainstream, most businesses know its benefits. Here are the benefits that companies see when they add AI to their automated testing processes.
Human error happens. It is not something that can be ignored. Even the most conscientious software tester will make a mistake once in a while when they are performing the tedious process of manual testing.
Automated testing removes some of the human error in this process, but automated tests are still written by humans, and steps can be missed. But when automation is combined with AI, it can find these missing steps, determine which parts of an application are being ignored, and let testers know. It can also update UI tests smartly without human intervention when a design changes.
With standard testing processes, bugs in an application can be overlooked or ignored for a long time until they finally become unbearable, or a customer complains. AI can catch errors in the code and bugs that manual testers may not see and that automated tests just ignore.
AI defect detection systems will recognize abnormalities in code automatically by comparing how an application runs to how it ran in the past. Sometimes it can correct these issues automatically or suggest a solution, but it will always find them, so they won’t be ignored.
Application development is fast paced in most companies. They must keep up with the features that competitors are adding and constantly add their own new features to stand out in the market. Often, software will be updated on a weekly basis.
What this means to testers is they must update their test plans for these changes with every update. If they are using automated tests, they must modify them, or they may fail. AI, on the other hand, can generate tests when an API changes or understand how a new UI feature is supposed to function, so tests can keep running without manual maintenance.
Every time an application’s code is modified, software testing must be repeated. If a business is currently doing manual testing, this will take some time. Even if a business uses automated testing, some of the tests may have to be updated for the new features. And if developers and QA are unsure of what the changes may affect, testing could comprise a full suite of regression tests just to be sure.
With AI-enabled automated tests, some tests, like those for defined APIs and the UI, can automatically update to account for changes. Integrating AI into tests will also help determine what the changes affect so that only targeted tests must be run.
Better Regression Tests
Regression testing ensures that new code changes don’t break the existing code. As time goes on, applications get more and more complex to the point that developers may be unsure what their changes affect. So, to be thorough, every automated test is run. But even those tests could miss parts of the application because they were created by humans prone to error.
AI can help determine which regression tests should be run for each change and find the parts of the app that are affected by the changes but are not covered by tests, so regression testing does what it is supposed to do.
Often, a website will look or function differently in different types of browsers because each has its own quirks. To find these differences, testers have to test the website in each browser manually and compare them against the design mock-up. This is a very time-consuming process that depends on human eyes for accuracy.
AI-enabled software testing will allow your tests to learn how your UI should look using computer vision and detect these differences and any visual bugs that may have popped up because of recent changes.
Anything that can speed up software development and testing processes will speed up the application release cycle, which means a company using AI-enabled software testing can provide new features to their customers quicker.
It was only a few years ago that manual application testing was the only way. Then automation became part of the process. Now, AI is finding its place in software testing. But it is not taking over the job. It is just one more tool to help QA testers do their jobs more efficiently and accurately.
Want to find out more about how AI can streamline your software development and testing process? To get started, book a discovery meeting with our solution experts.