AI in Software Testing: Where is it now & where it will be

As the innovations like Siri and Cortana have affected our day-to-day life, the impact on AI on software development is not far away. Machine learning model testing is the best example of Artificial intelligence in software development.

If you have spent any time in software development in the past decades, you might remember the days when manual testing was the norm. It’s still the case in many organizations.

Manual testing took several hours to test the software, and it still could not be tested comprehensively.

Thanks to software automation testing tools, the landscape changed completely and enabled testers to check software with minimal manual efforts, providing high-quality results.

However, when it comes to testing high-quality software in the shortest possible time, even testing automation is not enough because it takes time and effort to write and maintain test scripts.

With the help of artificial intelligence testing, we can achieve better results if you’re wondering how to read on! But before that, let’s briefly understand what AI is and how it works.

What is AI?

Artificial or machine intelligence is the intelligence performed by machines (computers) based on the observation of human behaviour. The AI-based system collects and learns from human behaviour data, including speech, voice, facial recognition, and more.

It then suggests answers to our questions based on its teaching or observation. If you have a detailed look, you may have noticed that all artificial intelligence testing techniques have very specific functions and are not generalized.

We still have a long way to reach that level when AI can do what humans can do, not just special activities.

AI and machine learning go hand in hand.

Since the 1950s, artificial intelligence testing has been used to find its ground in various fields, and the term that has made its way as an important part of AI is machine learning.

ML or machine learning is an important part of AI that uses pattern recognition technology. It finds patterns in the data you provide and then uses these patterns to predict future trends.

And the great thing is that machine learning model testing is not just limited to simple data retrieval and visualization. It can read and store a lot of complex information and find patterns in it.

For example, AI-based project management software can tell you how much work you can accomplish in the future by analysing your speed and work patterns using your past and present data.

Advantages of AI in Software Testing

The rise of test automation is similar to the adoption of agile methods in software development. It enables teams to deliver robust and bug-free software in small batches. Manual testing is limited to professional acceptance testing only.

Artificial intelligence testing along with DevOps helps Agile teams to ship a fail-safe product for SaaS/ cloud deployment via a CI/ CD pipeline.

In Software Automation Testing Tools, AI combines cognitive automation, logic, machine learning, natural language processing, and analysis.

Cognitive automation offers the benefits of various technological approaches such as text analytics, semantic technology, data mining, natural language processing, and machine learning.

For example, RPA (Robotic Process Automation) is a connecting link between cognitive computing and AI.

So, let’s see how AI testing has changed the traditional way of software testing.

Automatic visual recognition

One method of testing that is becoming increasingly popular every day is image-based testing using automated visual verification tools. The machine learning model testing can detect minor UI incompatibilities that are likely to be missed by the human eye.

The main purpose of UI testing is to ensure that each UI element looks good with the right size, colour, shape, and position and does not physically overlap with other UI elements.

All these visible errors can be verified even by simple ML testing without any tester intervention.

Automatically write test cases

The biggest application of AI and ML in test automation is automatically writing test cases for software.

We heard about web crawlers and "spidering" (browsing the web using automated and systematic scripts or programs) that helped us find 404 dead pages in earlier days.

Now, Machine Learning Model Testing have come a long way in learning the business usage scenarios of the applications under testing. All they need to do is the point to the software.

While learning applications, they automatically crawl and collect useful data, such as screenshots, HTML pages, and page loading times. Over time they collect enough data from the app to train the ML model for the representative samples of the app.

Improve reliability

Are you one of those people whose selenium tests fail due to small changes in the application (such as renaming or resizing the field) by the developers? If yes, don't worry. This is a problem that most testers face.

Now, artificial intelligence testing can complete the code and make it more reliable, so you don't have to change the test every time the developers make small changes.

The Machine Learning Model Testing can read changes in the application and understand the relationship between them.

Self-healing scripts like this observe the change in the application and start learning the change methods, and then you can recognize the change in runtime without doing anything.

ML scripts are automatically adjusted as the application develops, reducing the versatility and fragility of the test automation.

Low UI-based testing

Another change brought about by AI / ML is automation without a user interface. Non-functional tests such as unit integration, performance, security, and vulnerabilities are no exception.

The software automation testing tools can be used to generate tests at these levels. In addition, it is applied to a variety of application logs, such as AI / ML source code and product monitoring system logs.

The software ecosystem helps develop bug prediction, early notification, self-healing, and auto-scaling capabilities.

Final thoughts

AI-based testing reduces overall test costs, error, time, and scripting. Isn't this exactly what we want? There is no doubt that artificial intelligence testing and machine learning model testing are game-changers in the software industry, so they will soon become a trend in the market.

Now is the time for software teams to move to an AI-based approach to software development, testing, and management.

If you have any questions or would like to share your experience with AI in software testing, don't hesitate to contact us at nishil@qable.io.