Worried about end-to-end testing? This is a clear sign to upgrade your testing practices with AI test automation. This advanced step will help eliminate various traditional challenges like high costs, extended timelines, human errors, and limited scalability.
The inclusion of artificial intelligence in E2E testing will bring a massive transformation as the teams can execute the tests faster, improve accuracy, and expand the test coverage while reducing the manual efforts by a huge margin.
Not accustomed to AI test automation? Don’t worry! We are here for you. Our article will help you understand how AI can change end-to-end testing. We will also go through various advancements, benefits, challenges, and future prospects that you can expect in this market.
What is End-To-End Testing
Before we jump into the advanced end-to-end testing processes with artificial intelligence, it is very important to develop a clear idea about what end-to-end testing is in the first place. So, this is the process of verifying an app’s entire workflow, right from the start all the way to the finish. The goal? Ensuring a seamless interaction across multiple components like databases, third-party integrations, APIs, and multiple user interfaces.
Now let’s turn our focus towards some of the huge challenges that you will face while executing the traditional end-to-end testing:
- It is a very time-consuming and costly process, as you will have to write and maintain the test scripts manually with significant effort.
- Traditional end-to-end testing processes often tend to be flaky as they break due to minor UI changes. Therefore, it will require frequent updates, which will also cause inefficiencies in the entire testing workflow.
- We all know that manual testing massively struggles to cover all possible testing scenarios due to the massive resource constraints that are associated with it.
- Finally, the human involvement and environmental inconsistencies will cause unreliable outcomes during end-to-end testing with traditional practices and strategies.
How AI Can Change End-To-End Testing
All the challenges that we mentioned in the earlier segment about traditional E2E testing can be easily solved with the inclusion of artificial intelligence and machine learning. To shed more light over this segment, let us divert our attention towards some of the major ways in which AI can change end-to-end testing processes:
1. AI-Powered Test Script Generation
Do you remember the time when you had to manually design the test cases depending on the requirements of the application that you’re currently working on? You will be really pleased to know that artificial intelligence completely eliminates this process! This is because, with AI, you can rely on machine learning and NLP to automatically create test scripts.
It is also worth noting that AI testing tools can analyze the app’s behavior and user journeys to create optimized test cases and also constantly adjust them depending on the changing requirements of the application.
For example, an AI-driven testing tool can analyze your past session records and generate test scripts depending on real user interactions. This approach will be a key factor in reducing the manual scripting efforts.
2. Self-Healing Test Automation
You will agree that one of the biggest issues that you faced with traditional testing is the flakiness of the test cases. To solve this issue, AI integrates self-healing mechanisms where automation scripts will dynamically adapt to the user interface changes. Moreover, to implement this entire process, we do not require any form of human involvement.
For example, if a button’s XPath changes in a web application, you can use AI-driven testing tools like Mabel or Testim to detect these changes and update the test script accordingly to ensure that the test case works smoothly.
3. AI-Powered Cloud Testing
You will find a lot of AI testing platforms like LambdaTest, which can help you run your end-to-end tests on thousands of different browsers, real devices, and operating systems. The main advantage of this process is that you can verify the functioning of your application based on the physical parameters of your device while not investing millions of dollars in an on-site device lab.
Let us understand the influence of AI testing tools in real device testing with the example of LambdaTest as our reference point:
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 consists of multiple legacy devices and outdated browser versions, so that you can further expand the coverage of your AI test cases.
You will also be pleased to know that after executing the test cases, LambdaTest will automatically create a test report, which will provide you with multiple screenshots and videos so that you can get better knowledge about the faulty elements and implement the required troubleshooting steps before the deployment phase.
4. Intelligent Test Execution And Optimization
Using AI, you can optimize the test execution process by identifying the most critical test cases and prioritizing them depending on the risk factor. This approach will also reduce the redundancy in the test execution and ensure the testers can focus on the other creative and unique areas of the project.
For example, you can consider using AI-based test preparation tools like Functionize to analyze the past test results. Then, depending on the receipts inserts, you can execute only the relevant test cases to speed up the entire testing phase.
5. Predictive Analytics For Test Failures
One of the most effective approaches of AI test automation is using it to analyze the historical test data. Based on this analysis, it can predict the potential failures before they even occur during the testing process. Moreover, by identifying these patterns, artificial intelligence can also recommend collection steps so that you can implement them immediately.
For example, a DevOps team can use AI analytics tools so that they can predict which parts of the application are most likely to fail. This data will allow the testers to focus on fixing the high-risk areas so that they can ensure that the basic function of the application is available at all instances.
Challenges of AI in End-To-End Testing
Finally, it is very important for the testers to know about all the challenges that they will face while integrating artificial intelligence in modern end-to-end testing. To shed more light over this segment, we have mentioned all the common challenges regarding this segment:
- Implementing AI-based test automation will require multiple investments in terms of tools, infrastructure, and training. You will also need to ensure that you have proper workshops established to get all your testers and developers accustomed to this new practice.
- Although AI can help you to automate the functional test cases, it will massively struggle with complex business logic, which will require human judgment. Therefore, you cannot completely eliminate your human testing team.
- Depending on the quality of the testing data and the decision bias that the system can develop over time, you will see certain scenarios of misleading results like false positives and false negatives. In such a case, it is very important to have some form of human validation to ensure the accuracy of the overall infrastructure.
- Since the inclusion of artificial intelligence in the testing process is still a massively growing trend, you must be prepared to handle various uncertainties that might occur during the real world implementations. We highly recommend the testers to have some form of a sandbox model where you can test out these integrations before pushing them to the actual app.
- If you are working with AI-powered testing tools, you will require access to some form of test data. So, while creating this test data, you have to be very careful about your sensitive customer information, as this can cause serious breaches in terms of data security and other compliances.
- Finally, your teams need to constantly upskill themselves to keep themselves updated with all the upcoming trends and innovations in the segment of AI and machine learning. These upscaling techniques will not be the cup of tea for every tester or developer.
Best Practices For AI Test Automation
Finally, let us divert our attention towards some of the best practices that we recommend the testers to incorporate in test automation for integrating end-to-end testing:
- It is very important to understand that test automation and artificial intelligence is a heavy investment. This is because you will have to purchase multiple licenses, frameworks, and workflows that will be crucial for the execution of this process. So, it is very important to plan your revenue resources accordingly so that you can justify this transition.
- Although AI and automation testing aim to eliminate the human involvement in the repetitive testing scenarios, it is very important to understand that you will still require human supervision in certain areas like visual testing and exploratory testing. The perfect approach in this segment will be to maintain the proper balance between human experience and AI productivity.
- Since artificial intelligence is a continuously changing market, you should monitor it regularly to keep yourself updated with all the upcoming trends, innovations, and integrations. This approach will ensure that you are providing the best end-user experience to your customers at all instances.
- It is very important to document all your steps and approaches while performing AI end-to-end testing. This approach will ensure that you are already aware of the preexisting errors and also keep track of all the article modules. This step will also have a very important role in reducing the complexity of the future updates.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that AI is completely changing end-to-end testing by improving its speed, accuracy, and testing coverage. It also helps reduce the manual effort and execution costs that are associated with this process. AI test automation will also help integrate intelligence script generation, self-healing capabilities, predictive analytics, and autonomous test execution.
Although there are certain challenges that you will face while executing AI test automation, by using all the strategies and best practices that we have mentioned in this article, you can easily overcome them to create transformative test cases that can ensure the reliability, efficiency, and intelligence of your overall infrastructure.
Still unsure about AI in end-to-end testing? We highly recommend you to start with a small non-crucial test case. Based on the results that you achieve in it, you can scale accordingly to incorporate other testing areas as well.