You’ve probably heard about machine learning and artificial intelligence, but do they have a place in software automated testing? They might seem like the latest buzzwords making waves across industries, but are they changing anything?
Automated testing is not new. It’s about automating as much of the testing process as possible. Some of the more common automated test frameworks had at least a small portion involving machine learning or artificial intelligence (AI). In some cases, AI has become a core component of automation technology. However, most people don’t understand the benefits, how it works and how to use it properly in their tests.
Some crucial roles of Artificial Intelligence and Machine Learning in testing include:
Improving Automation Testing
ML is a technology that allows computers, processes, and algorithms to improve and make decisions based on experience automatically. Machine learning can be used to improve software testing while improving the quality of automated tests significantly.
Re-imagining Automated Testing
Employing AI in automated testing helps to re-imagine automation. This can be done by applying ML as an advanced tool for creating test cases that could otherwise not have been created using traditional automation methods or simply using AI to decide the success of specific test cases. As a result, significantly more test cases could be tested for the same amount of time or cost using AI-assisted automation techniques than manual techniques.
Finding Issues that Human Testers Miss
Most times, the human brain finds it difficult to process large amounts of data, make decisions quickly, and detect patterns. This is where AI and machine learning come in: Using neural networks and algorithms to create test cases, AI can identify structural problems or issues that would have otherwise been missed by manual testing. Machine learning can also be used to identify performance problems that may be difficult for humans to detect.
Improving User Experience
Other than testing the functionality of applications, automated tests can also be used to optimize the user experience (UX). This is done through a load simulation algorithm that uses machine learning to simulate how an application performs under heavy load and compares it against a baseline UX map. These methods are easily implemented, leading to a more user-friendly application.
Managing Test Resources
Automation is often used to complement manual testing and improve productivity by reducing human resources without compromising quality. Using AI to create test cases can also be used to filter out certain types of tests to reduce the workload and save budget. In addition, this technology can reduce the number of resources required for software product development because less money and time would be spent on creating unnecessary test plans.
Augmented Reality in Software Testing
AR is a technical term that refers to the integration of digital information with the environment in which we live. Essentially, AR provides the computer-generated “world” of apps, websites, and other digital technology with tactile, sensory details that enable users to interact with that world. Here are some examples of how artificial intelligence could be used in augmented reality:
v1.0 – Detecting failing integration tests in mobile apps having broken network connectivity by leveraging a mobile network device simulator (a smartphone) connected via Wi-Fi. v1.0 is a combination of data mining (i.e., detection of data content on images), artificial intelligence, and machine learning algorithms to detect failing integration tests in mobile applications having broken network connectivity by leveraging a mobile network device simulator (a smartphone).
v2.0 – Recognizing responses to an emergency alert test conducted in an augmented reality application. v2.0 is a combination of data mining, artificial intelligence, and machine learning algorithms to recognize the responses to an emergency alert test conducted in the augmented reality application. Android device manufacturers can use the result of this algorithm (e.g., Motorola, Samsung) to provide dynamic feedback on their devices when they receive the emergency alert messages carried within their mobile applications.
Designing Intelligent Test Cases with Machine Learning
When developing test cases for complex software applications, it can be hard for a human tester to identify which steps should be included in each test case, especially if the software has lots of different functionalities available (e.g., Facebook or Google). Machine learning can help in this regard, as it can identify patterns in the data that allows to classify test cases and detect situations where different test cases should be created. Testing teams can use machine learning to find these patterns more efficiently by setting up a so-called smart lab (i.e., an environment with enough compute and storage resources to perform machine learning calculations) that learns from known data sets.
An instance of how this may be done is using a machine learning model in an automated test framework that can learn from multiple data sets. Once it has learned the correlation between different data sets, it can automatically create patterns in new instances where they appear.
Diverse Applications of AI in Software Testing
In addition to the roles mentioned above, AI can also be used to help test teams detect potential software defects sooner and improve the quality of automated tests. Additionally, AI can help to perform more complex tests that would otherwise require human interaction (e.g., performing a usability test on a website to determine whether disabled users would have difficulties with it).
For instance, an automated test system can be used to perform extensive tests on an application and look for potential defects. When it finds a defect, it can report the breakdown of the defect to another system that would be able to diagnose the problem and fix the software.
A system based on artificial intelligence can have a higher accuracy rate than manual testing, but only when testing complex systems (e.g., complex business software applications). This is because AI cannot identify residual defects caused by things other than design flaws (e.g., typos in code, bugs/security holes in 3rd party libraries).
In addition, AI tests will also continue performing well even when algorithms are changing over time.
Artificial intelligence will continue to improve and replace manual testing jobs. The development of machine learning and AI has improved testing speed, accuracy, and efficiency, with AI expected to take over 40% of skilled labor jobs.
The bottom line is that AI-assisted automation is still evolving, but it already promises a lot of potential for many different areas of software testing. It’s not just a matter of when—but how companies can use artificial intelligence and machine learning to maximize the value, it brings to their organization.