How Machine Learning Can Be Utilized in Software Testing

Test Automation Creation

The QA and testing team need to write test cases. These are essentially mini-programs that test the software for very specific things. For example, the functionality of specific UI elements such as buttons.

If a change, update, or improvement is made to the code, then the test case must be rewritten in order for it to test the software in its current state. This process can be very time and resource intensive, as the QA and testing team must laboriously rewrite each test case after each code change.

Machine learning algorithms can be trained to automatically pick up on changes in the code of the software to help save time and reduce the need to test manually.

As more and more tests are conducted, machine learning software systems build models of how the software should work, gathering huge amounts of data that it can reference later on. This means it can automatically adapt to minor or major changes to the code, thus reducing the time spent on manual testing and case rewriting.

Test Automation Analysis

When a test case is run, it will produce a Pass or a Negative test result. It is then up to the QA and testing team to review the results and then determine the accuracy of those results.

Under certain test scenarios, it is not uncommon for the case to produce a false Pass (where the test scripts are not programmed to account for the possibility of a certain type of fault) or a false Negative (where the system misinterprets the existence of a new feature, function, or event as a bug or error). This means the QA and testing team has to specifically tell the system what to look for. Otherwise, it may lead to glaring oversights or misinterpretation.

By incorporating machine learning into the test analysis process, the QA and testing team can leverage computer vision and machine learning. What is computer vision? It is a field of artificial intelligence that uses image recognition technology to teach systems to interpret still and moving images, and the relationship between those images.

With enough training, the system will then learn what each image is, how they work, and how they relate to one another. Therefore, the system can accurately identify new (and existing) elements of the software, determine what they do, and decide how they work within the context of the entire software as a whole.

Also, because the system is more adaptable to both minor and major code changes, the risk of false Positives or Negatives is significantly reduced.

Test Automation Maintenance

Test maintenance is the process of monitoring and maintaining the test scripts (or cases) to ensure they test the latest version of the software.

One reason why test maintenance exists is because of selectors. Selectors are used to telling the software testing program what UI elements to interact with. The problem is that when creating software code changes, it also changes the selectors within that software, which means that each test script must be rewritten. Otherwise, it will produce test failures or proceed to analyze the wrong UI elements.

With machine learning, automated tests can detect what element changes or moves and then select the right elements in the next test. So, for example, if you change the placement of a Menu button from one place to another, the system will register the change in placement and analyze the button in its new position.

While more significant changes may require fine-tuning from the human QA and testing team, the time spent on adjusting test scripts is still much less than before. Even better still, if the test detects a problem, it can almost certainly identify it, determine the nature of the problem, and then recommend one or more solutions to rectify the matter.

Determine the Need for Machine Learning

In order to properly integrate machine learning into the software testing process, one must consider the reason for taking this initiative in the first place.

Will it help aid in the early detection of potential bugs, errors, and vulnerabilities? Will it help fill gaps in the test analysis that were previously difficult to test for through automation (i.e., slight variations in the way different users interact with the same software)? And most importantly, will it speed up the process of software testing and help clients get their products to market sooner?

By answering these questions, both you and your software development team will have a clear understanding of what machine learning in software testing can do and how it can help streamline the software development process and improve the quality of the final product.

Define the Technology Stack

In addition, the QA and testing team needs to define the technology stack to be used in machine learning.

There are many types of machine learning applications out there, and choosing the right ones is imperative to the success of your testing procedures.

For instance, there is a wide range of machine learning libraries, which contain a wealth of readily usable functions and routines. These libraries allow the QA and testing team to write complex software testing machine learning programs without writing all the code from scratch. This makes it easier for the team to ensure the software satisfies all functional and non-functional requirements.

Use Dedicated Experts

Lastly, the QA and testing team needs to have a dedicated data science team that specializes in machine learning and AI.

They should be able to write the machine learning algorithms, feed it the correct test data, monitor the software testing procedures, and ensure that the test results match the performance expectations of the team, the client, and the relevant regional laws that the software must abide by.

By having dedicated artificial intelligence experts at the helm, they can make sure the systems are aiding – and not hindering – the software testing processes and that it is enabling autonomous testing to be more effective and accurate than ever before.