Do you remember the time when artificial intelligence was just an experimental concept? Well, it’s no longer the same scenario! Now, it has become a very important part of modern software testing as it can transform how your tests are executed, monitored, and optimized. You can also use AI testing tools to increase the efficiency, accuracy, and adaptability of your entire testing environment.
Unfamiliar with AI testing tools? Don’t worry! We are here for you. Our article will help you understand the most powerful AI-powered test automation platforms that are currently available. During our discussion, we will also explore their capabilities, major benefits, and all the practical applications.
Why Use AI in Test Automation
Before we start discussing all the advanced tools for introducing artificial intelligence in test automation, it is very important to develop a clear idea about why should we even bother about integrating this process in the first place. To shed more light over this movement, we have mentioned some of the major benefits of introducing artificial intelligence in modern test automation:
You will have access to self-healing test scripts, as artificial intelligence can automatically detect changes in the application’s user interface. It can ensure that the scripts can adapt dynamically based on all these changes.
With artificial intelligence, you can also use predictive analytics to forecast potential areas of risk. This insight will help you highlight where testing efforts should focus and guarantee that the basic functioning of the application is available at all instances.
Using natural language processing, you can write test cases in plain language. This will make the test scripting process even more accessible to the non-technical members of the team. This approach will also help eliminate the learning curve that comes with any new software or strategy integration.
You can also use intelligent test generation to automatically generate test cases depending on user interactions, application workflows, and past defects. This is a very important process to ensure that your entire testing infrastructure properly reflects the modern real-world use cases.
Finally, artificial intelligence will help implement enhanced test maintenance to reduce the manual efforts required to maintain the test suits. This approach will also guarantee the robustness and reliability of the test cases over time.
Ideal AI-Powered Test Automation Platforms
To help the new testers incorporate AI-powered testing tools, we have created a list of some of the most popular test automation platforms that help integrate artificial intelligence and machine learning. While creating this list, we have ensured that we are incorporating all the general use cases that are currently available:
1. LambdaTest
LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 5000+ real devices, browsers, and OS combinations. This device farm also provides access to various outdated browser versions and legacy devices to further improve the coverage and compatibility of the testing infrastructure.
You can easily integrate LambdaTest with various automation platforms like Selenium, Appium, and others to initiate advanced testing processes like automated cross-browser testing, AI E2E testing, and even automated unit testing.
Some of the advanced features of LambdaTest include native bug tracking and real-time tracking of test cases. Moreover, another major feature of this platform is that it will automatically provide you the comprehensive test reports consisting of screenshots and videos to find the faulty elements present in the application infrastructure.
So, who should use LambdaTest? If you want to test the functioning of your application on a comprehensive range of real devices while implementing various advanced test automation practices, this is the best option for you.
2. Testim
This platform deploys AI to simplify the taste creation, execution, and maintenance stages. It also deploys AI-driven self-filling capabilities so that we can automatically adjust the tests whenever the application elements change. While using this platform, the testers will have access to the following benefits and features:
- You can use AI-based element recognition to find all the elements on your user interface and redirect the test cases accordingly.
- Using self-healing tests, you can ensure that all the existing test scripts can dynamically adjust to the changing parameters and requirements of the application that you’re currently testing.
- You can also deploy continuous integration and continuous deployment within the testing infrastructure to ensure faster feedback loops and proper implementation of agile methodologies. It will also help you ensure that bugs are removed as soon as they appear in the core infrastructure of the application.
- It also comes with a visual editor so that you can create automated test scripts for all the UI elements and their proper placement verification stages.
So, who should use Testim? If you are working in an agile team that requires quick turnaround times for test creation and implements low test maintenance, this is the perfect tool for you.
3. Functionize
Functionize uses artificial intelligence to create robust, maintainable test cases very quickly. It also uses natural language processing so that you can create tests in plain English language which can then be translated by the internal layer of the platform. The primary goal of this process is to allow the new testers to get started with the testing process as soon as possible. While using this tool, you will have access to the following features:
- You can use the Natural Language Test Scripting process to create the test scripts in a language that can be understood by every member of the team.
- It employs autonomous test execution and analysis to reduce the human involvement in the process. This is a very important parameter to ensure the proper scalability and productivity of the entire environment.
- Using self-healing capabilities, the tests can automatically adjust to all the changes that will be introduced into the environment and the application as a whole.
- Finally, predictive failure analysis will go through the past test data and reports to analyze the most common areas of the application that are prone to failure. Based on this data, you can concentrate all your testing efforts to these areas.
So, who should use Functionize? If you are working in a team that wants to involve both technical and non-technical stakeholders in the test design and execution process, you must choose this tool.
4. TestCraft
TestCraft, which is powered by artificial intelligence, is the best option to simplify the test automation process for web applications without the coding part. This platform also dynamically adapts to UI changes so that you can massively reduce the test maintenance phases. Let us now divert our attention towards some of the major features that will be available with this platform:
- It comes with a visual codeless test automation framework so that you can easily create the automation test scripts without actually learning the coding phase.
- The AI-driven automatic test updates ensure that you do not have to put in any manual efforts to update the test scripts whenever you add or remove any feature from the core infrastructure of the application.
- This tool will be the best option if you aim to implement immediate execution of cross-browser tests. This is a very important parameter for modern applications that need to be working perfectly on any browser chosen by the end user.
So, who should use TestCraft? If your organization has limited coding expertise but requires stable and maintainable web tests, you must seriously consider this option.
Challenges and Considerations
Despite the massive advantages of using AI in software testing, you must understand that it will also have its own share of challenges. To ensure that new testers do not suffer from these challenges, we have mentioned some of the major ones that you must be careful about, along with their most effective solutions:
- The initial integration of AI testing tools will require a steep investment in terms of licensing and training fees. So, you must have a clear understanding about your returns on investment and other metrics to justify this shift.
- Although certain AI testing tools do not require coding and other expertise, there will be a learning curve involved with a completely new software. Therefore, you must prepare yourself accordingly to ensure they do not hamper the testing phase or the expected delivery outlines.
- If you’re trying to integrate AI testing tools into legacy testing software and existing workflow, you might face certain challenges in terms of compatibility issues. You must have a thorough idea about all the prerequisites to ensure a smooth transition.
- Although AI testing does not require the involvement of human testers, it is very wise to have some form of human supervision to ensure that any system failures or malfunctions do not completely destroy the test execution process. The perfect approach to this is to maintain the balance between human knowledge and artificial intelligence’s efficiency.
The Future of AI in Testing
Do you feel that AI-powered software testing has reached its peak? You are massively mistaken! In the coming years, artificial intelligence will further expand its scale in terms of software testing and influence the following areas as well:
- It will increase the autonomous test execution process. During this process, it will also identify various real-world scenarios to adapt the test execution process. It will perform all these without the slightest involvement of any human tester.
- AI-driven analytics will help you to predict software failures even before the deployment of the source code. Therefore, this process will further help reduce risks of application failure or complete crashes after the deployment phase.
- AI tools will continue to become more and more deeply embedded within the development pipelines. This continued integration will help gather real-time feedback to implement proactive optimization. The result? You will be able to deliver a software quality that was earlier unimaginable.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that AI-powered test automation platforms will show how the future of software testing will look like. Using its efficiency, accuracy, and flexibility, the testers can massively improve the reliability and quality of the test environments.
You must carefully select and integrate the right testing tool so that you can improve your testing qualities to deliver robust, scalable, and high-quality software to your customers.
Finally, you must keep yourself updated to all the new upcoming technologies in the segment of AI-powered testing. These integrations will help you ensure that you’re continuously providing the best end-user experience to your customers to build a positive brand reputation.
