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Intelligent Test Automation Provides Accelerated Testing And Higher Quality Deployments

Published Date
Author Team Copado

Intelligent test automation is a lot more than scheduling tests. It provides real value through AI, machine learning, and intuitive automation.

Intelligent test automation gets a lot of hype, but a lot of test automation solutions don’t fully incorporate artificial intelligence and machine learning. They simply make it easier to schedule the tests on your software. While that's a time-saver, it's not intelligent. That would be like saying a conveyer belt in a factory is intelligent. Sure, it moves on its own, but it's not adding anything of value without human intervention.

True intelligent test automation is scalable, intuitive, and adaptable. It means that if the program you’re testing changes, your test won’t break. Instead, it will adapt to the changes in its environment by leveraging advanced tools that allow it to think like a human. 

What is Intelligent Test Automation? 

Intelligent test automation (ITA) is a tool for seamlessly testing software without relying on manual processes. While there are a lot of different ways to reach ITA, it usually combines some level of artificial intelligence, machine learning, and automation. 

Automation

Many manual tests are predictable, repeatable, and high volume. Leveraging automation allows you to schedule these tests either based on actions (like pushing new code) or timetables. However, it's important to remember there's a big difference between basic automation and ITA, which combines other components to make this far more intuitive.

In basic automation, you'd set a static test to run with every piece of code, but if the code changes, you need a new test. With ITA, the test will adapt to the changes.

Artificial Intelligence

There are a few different machine learning methods that may be leveraged in ITA. Three of the most common include:

  • Supervised: A human teaches a program what combinations result in expected outcomes.
  • Unsupervised: The computer uses data to understand outcomes.
  • Reinforcement: The machine is given a problem to solve and must create a solution.

These methods help programs understand cause and effect so they can make informed decisions.

Machine Learning

While AI and machine learning are often paired, they are not the same thing. Machine learning is a subset of artificial intelligence that uses specific methods to guide decision-making.

AI is a bit loftier. While machine learning is part of AI, it's not the only component. AI describes a wide range of methodologies designed to simulate the human thought process, including ones that do not yet exist.

AI also describes the way machine-learning or other programs can “feel” intelligent. That generally means using them feels natural – and the actions they take are genuinely helpful.

 

Combining these three components has been revolutionary when it comes to software development. It takes standard, time-consuming steps out of human hands so testing can keep up with the rest of your development. No longer will people be tempted to skip tests due to time crunch or expense. Instead, testing becomes a source of value because it lets you build better software faster and more efficiently. 

How This Combination Improves Results 

The combination of machine learning, artificial intelligence, and automation in software testing is key because each part serves to provide more value. They need to work together for you to make the most of your process. Let's say that you have a standard test you run with every release. A strong ITA program would work as follows: 

  • Automation: The test runs based on set parameters with no need for a manual script or test creation. 
  • Machine learning: The program knows, from prior data and experience, what results are normal and which will require remedial action.
  • AI: If program changes break a test, AI will adapt it – making the test self-healing – to run under the new parameters. 

All of this is important because it makes testing scalable. If you accelerate your release schedule or need to make changes, you can quickly adapt your testing process. ITA grows with your organization, so you don't have to rewrite tests or update your entire testing strategy every time your software changes.

A good ITA platform is also easy to learn. As more and more programs go low-code or no-code, tests need to be just as easy to set up and maintain. If you require an expert to fix the software your non-technical people use, you're losing out on a significant opportunity to streamline time and effort. The ITA program you use should ideally be system-agnostic, so it can run as well on Salesforce as it does on the rest of your cloud architecture.

Intelligent test automation is far more than basic automation. It's a tool that will work with your existing programs to adapt your tests as you change your business processes. It's a way to improve your software without an exhaustive search for long-term expertise or relying on labor-intensive, unscalable manual testing options.