What is AI in Software Test Automation?
There are two main areas of AI that are used to support software testing: self-testing and self-healing systems. These are two areas where you can find a number of tools that can provide you with the support you need.
Self-testing and self-healing systems
Self-testing and self-healing systems in software test automation can help your team be more productive. This is because it allows you to identify issues early in the development cycle. It also helps you to increase your team’s agility.
Automated testing allows your organization to quickly and easily share test results with stakeholders outside the development team. You can also make sure that your software is tested on all available hardware and operating systems.
A self-healing system can scale an instance as a service degrades. In addition, it can react to application downtime. The technology also provides you with self-managed environments, which can be a boon for testers.
Automated test automation with self-healing is a major advancement in QA. It can help to reduce redundant code, improve the upkeep of your test scripts, and identify issues before they become a disaster.
For instance, you can use a self-healing engine to automatically map and remap objects. As a result, your tests will run faster and better. Additionally, you can automate test execution so you never have to worry about running into bugs.
Another self-healing feature is retry logic. If a call fails, the retry logic will allow you to try again. By making this step automatic, you can dramatically cut down on manual troubleshooting and the time and cost it takes to redo these tasks.
AI-based tools can easily support your workflow
If you’re working on a software test automation project, you can leverage AI-powered tools to improve your quality assurance efforts. These tools can help you develop and run test cases, analyze apps, and trace flaws spontaneously.
The use of AI-powered testing tools can help you move away from a manual test automation workflow. They can also help you increase the efficiency of your testing process.
One of the most powerful and versatile AI-based tools is Selenium. This tool uses natural language processing and machine learning to automatically identify bugs. It can test across multiple browsers, browser versions, and devices, and provides real test cases.
Another tool to consider is ZeuS Automation. This platform combines ML with functional and regression testing. Scriptless and robust, it can easily support end-to-end automation.
Another AI-powered testing tool is TestCraft. The system allows you to visually create automated Selenium-based tests. You can run the tests on thousands of real devices, browsers, and combinations of them.
Vertex AI Prediction offers continuous modeling and prediction, which enables developers to analyze and improve their existing model architectures. The platform supports BigQuery ML, triggered model retraining, and Kubeflow Pipelines.
Using an AI-powered testing tool can also help you find and track duplicate errors. By analyzing user behavior, AI can tell you whether a change has caused a test to fail.
AI-augmented software test automation
AI-augmented software test automation tools enable software engineering teams to accelerate testing. This technology can help unlock enhanced test coverage, faster time-to-market, and improved business continuity.
The use of AI for automated test creation is increasing. AI-based products can speed up the development of unit tests, improve overall test coverage, and even automate the creation of API tests.
Testing is a critical part of the software development process. It can uncover runtime and security vulnerabilities and ensure that applications are performing as expected. But the task is also time-consuming and labor-intensive. As an application grows, testing becomes more complex.
The demand for more fast software deployments is causing the testing process to become more complex. Many organizations are struggling with scaling their test automation. To meet the demands of the market, they must ramp up in speed, test coverage, and agility.
Sealights’ Test Gap Analytics is a cloud-based service that uses artificial intelligence and Big Data analytics to identify areas that need testing. This technology can detect tiny changes in code, so it can be a key part of a DevOps process.
For example, Sealights’ administrators can define “quality gates” based on overall quality policy. These gates allow AI-enabled bots to review code coverage, test statuses, and other metrics to make recommendations for the next steps in the testing process.