Test automation has considerably improved software testing in many ways. For starters, it allows software testing to be performed faster than manual testing. This helps the testers to complete the tests in a shorter time, resulting in quicker product release. It also provides other benefits such as consistency in test results, improved coverage, lower expenses, reusability of test scripts, and continuous integration.
Artificial intelligence, neural networks, and machine learning are one of the most trending topics in the tech field, and software testing has joined the trend. Many automation tools are now employing artificial intelligence to improve software testing and reduce the tedious aspects of quality assurance.
This article talks about the benefits of artificial intelligence to software testing with examples of automation testing tools that incorporate artificial intelligence.
How AI Can Impact Test Automation
Despite being a relatively new addition to the software testing process, AI based automation testing tools are already revolutionizing the way automation testing is performed thus playing a significant role in the process of software testing. By leveraging the power of artificial intelligence, these tools are able to offer advanced capabilities and improved efficiency, resulting in a better overall testing experience. Here are some ways that artificial intelligence is impacting test automation.
Test Case Generation
Artificial intelligence can create test cases to test the software without the input of human testers. It can analyze the source code and generate test cases automatically. It can also prioritize test cases based on factors, such as code coverage, risk, and the likelihood of defects or bugs. This will help you to ensure that the most critical cases are treated first, allowing you to improve the effectiveness of software testing.
You can also use AI to generate test cases that are more likely to identify bugs and defects in the application. It can also monitor the code and automatically create test cases as the developers make changes to the code.
Artificial intelligence can make test execution more efficient and accurate in various ways. It enables parallel test execution by scheduling and executing multiple tests simultaneously, thereby reducing the testing time. It can also monitor the test execution in real-time to identify any bottlenecks and make necessary adjustments to improve the test execution.
Artificial intelligence can optimize testing by analyzing the system under test (SUT) to identify and remove redundant or unnecessary test steps. This helps the test cases to run faster. It can also monitor the system to predict when it will likely fail and prepare accordingly to schedule maintenance or testing.
Defect prediction is easier with an AI-based automation tool. It can review the system under test to find patterns or anomalies that indicate defects in the code. Artificial intelligence can also utilize machine learning algorithms to study historical data on defects and identify patterns to predict future defects.
Constant Learning from Data Production
Artificial intelligence and test automation work together to observe how the users interact with the application to identify normal user behavior. Then it uses the insight to create test cases based on actual life data. This helps the tool perform data-driven software testing.
Examples of Testing Tools Which Have Successfully Incorporated AI Into Their Processes
Here are some examples of testing tools that employ AI for software testing.
Applitools is a cloud-based automation tool that helps users to test the visual appearance of web and mobile applications. It helps testers to identify the visual defects in web and mobile applications. It employs an AI-assisted computer vision technology (also known as visual AI) to find functional and visual regressions in the system. It also has a reporting feature that allows users to develop test results.
TestRigor is a cloud-based automation tool that helps testers create AI-powered test scripts using basic English commands. It comes with a broad range of integration for fast execution on desktop, mobile, and web platforms. It also features an AI-based test optimization that identifies the critical test cases and parts of the system with a higher likelihood of defects. You can also integrate it with continuous integration and continuous delivery tools, such as Jenkins, GitLab, and Bamboo.
TestSigma is an open-source automation tool for testing on web, mobile, and API platforms. It employs natural language processing (NLP) to create and execute test cases in basic English sentences. The NLP converts the natural language inputs into automated test scripts, making it easier for non-technical users to develop test cases. It supports integration with continuous integration and continuous delivery tools, including Jenkins, Travis, GitLab, and Bamboo.
Functionize is an automation tool that employs machine learning and artificial intelligence to create, execute, and manage different types of software testing. It supports codeless automation, allowing users to create test cases without prerequisite coding knowledge. It also employs natural language processing (NLP) to understand user intent and generate test cases based on user actions. Functionize can be integrated with continuous integration and continuous delivery tools such as Jenkins, Bamboo, and GitLab.