Struggling to keep up with shorter release cycles while maintaining software quality? Yes? Then you, too, are in the trap of traditional testing methods that are time-consuming, inefficient, and prone to human errors.
So what’s the solution? It’s time to upgrade to AI E2E testing! This is an approach that uses advanced technologies like AI and ML to create and optimize the end-to-end testing processes for better accuracy, flexibility, and speed.
Never heard about E2E AI testing? Don’t worry! We are here for you. Our article will help you understand the fundamental concept of AI E2E testing, along with its benefits and challenges. We will also go through some of the major strategies that can help you to adopt this process within your testing environment.
What Is E2E Testing?
Before we divert our attention towards the influence of artificial intelligence in AI end-to-end testing, it is very important to develop a proper understanding of the traditional E2E testing processes and why it is an important part of the software development life cycle.
This workflow helps you to validate the entire working of an application, right from the user interfaces to the databases, and also the back-end services. Moreover, while implementing the verification process, you have to run the test cases in real-world conditions. The primary principle of this process is that it does not focus on individual components in isolation but aims to replicate user journeys and business processes.
The end goal? Guaranteeing that all integrated parts of the application behave as expected in a production-like environment.
Let us now divert our attention towards some of the major reasons that justify the integration of end-to-end testing in the modern testing workflow:
- This process will verify the combined functioning of the subsystems. This is because a single broken link or a misconfiguration in any one service can derail the functioning of the entire application.
- It can also help you to simulate the user’s perspective. With this method, you can ensure that all real-world user interactions are smooth and reliable throughout the application.
- If you’re creating applications that are in the domains of finance or healthcare, this process becomes even more useful as it will help you to meet the regulations which demand proof of the system’s reliability and data correctness.
- Finally, modern systems have multiple microservices, third-party integrations, and distributed environments that need to work correctly at all instances for reliability and stability. End-to-end testing is a crucial step towards achieving this goal for multiple components.
Why Use AI E2E Testing
Now you might ask, since E2E testing is already so efficient and integral for the testing process, why should we even consider integrating artificial intelligence into this process. To justify this step, we must first divert our attention towards some of the major shortcomings of traditional end-to-end testing:
- If you’re working with conventional automated end-to-end tests, you will need access to frequent script updates whenever the user interfaces or APIs of the application change. Therefore this manual effort will harm the development velocity and can also impact the application’s functioning.
- If you’re still relying on manual or traditional automated tests for running end-to-end testing, there is a very high possibility that you are neglecting the subtle user scenarios or edge cases due to constraints in human time or expected loads.
- If you’re working with hardcoded test data, you will realize that you are encountering repeated and predictive test executions. This is simply insufficient to find real-world failures as you cannot adapt to the dynamic environments of multiple use cases.
- Working with legacy tests, especially the fully manual ones, will eat up a lot of time which will, in turn, delay the feedback loops while implementing continuous integration and continuous deployment pipelines.
Now let us divert our attention towards how you can solve all these challenges of traditional end-to-end testing processes by the inclusion of artificial intelligence:
- AI can automatically help you to adapt the test cases depending on minor UI changes, identify relevant elements on the screen, and also update the scripts without requiring any form of human involvement.
- Machine learning models will also help you to predict which test cases or workflows are most likely to fail or have a crucial user impact. It performs this process by analyzing the historical test data. After this, you can focus all your testing resources to these areas for ensuring the basic functioning of the application.
- Artificial intelligence will be very important to automatically analyze the system’s behavior for finding anomalies, patterns, and potential regressions that will not be found in traditional testing scenarios. This approach will also provide you with a complete idea about the functioning of the application infrastructure.
- You must remember that your AI systems will learn from each execution cycle and refine the test strategies accordingly. During this process, it will also discover new UI cases and prevent the recurring defects from occurring again and again.
AI-Driven E2E Testing Architecture and Workflow
Excited about implementing AI E2E testing in your testing environment? To help you with the implementation process, we have mentioned the workflow for this process and all the steps that you must follow in the correct order:
1. Test Design & Generation:
The entire implementation process begins with specifying the application’s UI needs, user journeys, and historical bug data. In this step, the AI engine will analyze the UI and logs to suggest test scripts or automatically generate them as per your requirements. These scripts will portray various real-world use cases.
2. Test Environment Setup:
After setting up the test script, you have to proceed towards the test environment setup in a containerized or virtual environment, which will replicate their production style. Here artificial intelligence will help you create these environments using machine learning insights to automatically select relevant system dependencies or test versions.
3. Test Execution & Monitoring:
In this phase, you have to feed the test system the AI-generated test scripts that you have created in the previous step. Along with that, you should also mention all the external tools for load simulation or the scenario creation steps.
Based on the provided input, artificial intelligence will optimize the order and frequency of test executions. It will also consider the risk assessment reports and resource availability for this process. Throughout the implementation process, artificial intelligence and natural language processing systems will monitor the system metrics to detect anomalies in real-time and notify the user accordingly.
4. Analysis & Reporting:
You have to now proceed towards analyzing the test reports using all the relevant logs, metrics, screenshots, or videos from the test runs. Machine learning algorithms will process these vast volumes of data and also categorize these failures. This analysis based on segmentation will allow it to identify the root causes and generate actionable insights.
5. Continuous Improvement:
The final step in this process is to implement the provisions for continuous improvement within the test cycle. Here, you can add new application features, production traffic, or user statistics as per the requirements.
Depending on your inputs, the AI will update its predictive models, refine risk-based prioritizations, and continuously evolve the test suits. The end goal for this implementation is to cover all the emerging usage patterns or potential defects that might appear in the infrastructure in the long run.
Apart from all the steps that we mentioned above, you must remember that these inclusions might change depending on the unique needs of the app that you’re currently working on. So, it is very important to have a clear conception about what you want to achieve with the entire AI E2E testing inclusion.
Best Practices for AI E2E Testing
Finally, let us divert our attention towards some of the best practices that we request the testers to incorporate in their end-to-end testing processes while using artificial intelligence. These inclusions will be very important to ensure a smooth integration and also optimal results throughout the entire workflow:
- Instead of using AI E2E testing in the entire testing process, you must choose a pilot project, like a less business-critical component or a well-defined user flow. Based on the lessons and the gratuity scale, you can expand the test cases accordingly.
- It is very important to have a clear idea about all the goals that you want to achieve with AI end-to-end testing. It can be either reducing the test maintenance costs, boosting coverage, or speeding up the release cycles. These metrics will help you measure the success of the workflow.
- We highly encourage you to invest in a data pipeline that can unify the test logs, bug reports, and usage statistics. The richer and clearer the data, the better the AI model will perform in real-world use cases.
- You must remember that machine learning models in quality assurance aren’t “set-and-forget” forever. Rather, it is a continuous monitoring step to ensure the accuracy of predictions and retain them on new data for better results.
- You can implement automated scheduling of AI tests within continuous integration and continuous deployment pipelines to receive immediate feedback while reducing the cost and complexity of defect resolution processes.
- To integrate AI QA testing and real device testing with this process, you can consider using cloud platforms like LambdaTest.
LambdaTest is an AI-native test execution platform that lets you perform manual and automation testing at scale with over 5000+ real devices, browsers, and OS combinations. The following code snippet will show how you can perform automated end-to-end testing with this platform:
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that AI E2E testing will promise a new world of streamlined, dynamic, and highly efficient automation testing. By integrating all the best practices and strategies that we have mentioned in this article, you can massively reduce the test maintenance overhead, improve the test coverage, and accelerate your feedback loop.
However, to ensure that you’re utilizing the full potential of the entire workflow, you must implement proper data management and model fine-tuning algorithms. This approach will also ensure that your organization is ready for the upcoming trends in this segment while ensuring the cost efficiency of the overall implementation.
Still skeptical about the influence of AI E2E testing? Start with a small unit test and understand how it influences the overall functioning. Based on the reports, scale accordingly to influence the entire app’s workflow.
