When you hear “Artificial Intelligence,” (AI) what do you think of?
You might picture advanced humanoid robots or futuristic technology from sci-fi movies. But as it turns out, this “futuristic” technology is already integrated into our lives. It exists in our phones, televisions, and homes to make everyday tasks simpler.
When it comes to software testing, AI can be implemented into your testing initiatives to increase efficiency. And while “AI in testing” is a buzzy phrase that may prompt some skepticism, it can provide real value and streamline your testing efforts.
But what exactly is AI, where is it headed, and how can it optimize software testing? Keep reading to find out.
What is Artificial Intelligence?
Artificial Intelligence is a system with increasingly self-learning capabilities that can supplement human cognition and activities. It does this by understanding the environment, solving human problems, and performing human tasks.
For example, Google Maps uses AI to monitor traffic patterns and predict the best routes for you to take. Netflix also uses machine learning (a subset of AI) to power its recommendation algorithm, which suggests shows suited to your preferences.
AI systems like these operate by feeding data into intelligent algorithms that can learn and improve by analyzing patterns within the data. And the more data these algorithms receive, the better they get at understanding the environment and predicting patterns. These systems also can be trained to perform both simple and complex tasks, depending on pattern matching algorithms.
Will AI Take Over Our Jobs?
There’s a looming fear that AI will take over our jobs … and then the world. And while this makes for a great movie plot, the reality isn’t so scary. Human testing assisted by AI is still the best practice and will remain that way for the foreseeable future.
The Chief Scientist at test.ai, Tariq King, recently spoke about the future of AI in “Lighting Strikes the Keynotes,” at the 2022 STAREAST Software Testing conference. King pinpointed our biggest problem as being an abundance of bad software that people no longer trust. And with all this poorly produced software, there’s concern that AI could be an efficient way to create more of it.
But King went on to discuss a promising alternate reality: a world in which an abundance of good software is produced, and AI is kept stable by testers. Once the “AI revolution” occurs, we can build higher-quality software and AI systems and use that AI technology to enable trusted software. And through all of this, a new subset of testers will emerge as the heroes who oversee and support these improved AI systems.
So, while the future of testing will begin to change, our world isn’t becoming a sci-fi movie just yet.
How AI Can Optimize Your Testing
Software testing has evolved rapidly in the past few decades. It began with manual testing, moved to early automated testing, agile testing, and then continuous testing. And now, it’s reached AI-augmented autonomous testing.
Achieving autonomous testing may seem like an impossible goal, but you can start implementing AI features that will create the foundation for it. AI can optimize your testing by accelerating test creation, expanding test coverage, and reducing test maintenance. But what does this look like? Let’s see some examples.
Object recognition is a form of intelligent design, which recognizes new objects and updates to add to the DOM without manual effort. There’s also framework generation that automatically scans your application to recommend a test framework.
Business process automation is an example of intelligent test execution, which helps to automate business workflows for end-to-end testing. Finally, self-healing is a form of intelligent test maintenance, which dynamically updates your test suite when your application changes or evolves.
SmartBear Examples of AI in Testing
TestComplete, SmartBear’s UI test automation tool, has an AI-powered feature called the Intelligent Quality Add-on. It has intelligent capabilities such as self-healing tests, optical character recognition (OCR), and ML-based visual grid recognition.
These self-healing tests can automatically identify unexpected errors due to dynamic properties and recommend a better alternative. This prevents tests from failing, but more importantly, saves you time you would’ve spent reviewing these errors. OCR also helps you interact with content on complex applications by identifying objects based on text contents instead of their properties.
You also can be more efficient with ML-based visual grid recognition, which helps you validate data against complex objects like grids and tables. It does this by recognizing data within tables and verifying its contents against a baseline copy.
In the future, TestComplete seeks to introduce a higher level of AI to help you achieve autonomous testing. It will use AI to generate test cases, run tests, and modify test cases (even if the UI changes), all without human involvement.
The Future Has Arrived
AI technology still has a ways to go, but it’s already starting to make our lives easier from Netflix recommendations to software testing. And if we work to improve upon and utilize AI technology, things will only get better. Now start to embrace AI and be the key to shaping the next phase of test automation.
Don’t let the future of testing pass you by. Start a free TestComplete trial today with the AI-powered IQ add-on.