AI Testing: Reduce Manual Effort In Enterprise Apps

Still not using AI testing for your software development processes? It can be a massive risk considering the quickly expanding complexity of modern apps. It is even clearer when you're working on enterprise apps that require proper scalability, stability, and performance metrics.

With the help of AI testing, you can use the power of AI and ML algorithms to streamline the QA processes. This will also help you to cut the overall development costs and reduce the manual effort during the testing phases.

Not familiar with AI testing? You do not have to worry! We are here for you. Our article will help you understand how you can use AI testing processes to reduce the manual effort in enterprise apps. We will also help you go through some of the best strategies and practices to further streamline the implementation of this process.

What Are The Challenges Of Enterprise App Testing

Before we start exploring the role of AI testing in modern enterprise app development, it is very important to have a clear understanding of all the challenges that you face in this process while using the present traditional testing practices:

Scale And Complexity

If you are working on enterprise applications, you must know that these apps spread across multiple modules to integrate with various APIs and databases. They also typically serve huge audiences with different personas. If you are testing these systems manually, you will have to go through extensive effort to set up the testing environment, write exhaustive test cases, and also ensure regression across all the available components.

Frequent Updates And Releases

As app developers have started to use DevOps and agile methodologies, updates are pushed to the application almost every week. Each of these updates, whether it's a bug fix, user interface enhancement, or a new feature, will require regression testing.

This is because with this practice, you can ensure the stability of the infrastructure. However, the entire process will become almost impossible to conduct manually due to the rising complexity of the application with every update.

High Cost And Time Consumption

While you're performing manual testing, you will consume a lot of engineering hours, which will, in the long run, delay the release cycles for the applications. Apart from this, there's a high possibility that the human testers can miss the edge cases or introduce inconsistencies within the framework of the app.

Test Maintenance Overload

You have to admit that maintaining and updating the test script for the application after every release is a very tiring process. This is because the changes in the user interface of the application or workflows can cause the existing test scripts to break completely, which will, in turn, destroy the dependency of the testing environment.

If you fail to properly implement this process, it can also cause increased effort just to keep the test suite functional and ensure the consistency of the entire infrastructure over a longer period of time.

What Is AI Testing

Since we are claiming that AI testing will help completely eliminate the disadvantages of using traditional test practices, it is only justified that we define what is AI testing and how it can influence your test cases.

It is the process of using artificial intelligence and machine learning to automate the design, execution, and maintenance of software test cases. Compared to traditional automation tools, which use predefined test scripts, AI testing systems can learn from data or may adapt to changes, and even self-heal when the application's behavior changes.

To further help you understand the role of AI testing in the modern software testing process, let us divert our attention towards some of the major capabilities of this process:

• With AI testing, you can generate test cases using natural language. This is because it can automatically understand the plain English documents and transform them into a test script that the system can understand and execute.

• With every update, when features are added or removed from the application, AI testing can help you self-heal the test scripts so that they can automatically adjust to the changes that you have introduced in the application.

• It allows you to implement predictive analytics by going through the historical test data and the previously executed test reports. Based on the information that it will gather, it can predict the areas of the applications that are most likely to fail.

• For a very long period, visual and exploratory testing were almost impossible to automate as the traditional testing practices weren't able to verify these criteria. However, with the advanced capabilities of AI testing, you can introduce automation testing in these areas as well.

• Depending on the high-risk areas that AI testing will find out, you can redirect all your testing efforts accordingly to ensure that at least the basic infrastructure of the application is functional at all instances.

How AI Testing Reduces Manual Effort

Let us divert our attention towards how the proper use of AI testing helps reduce manual effort within the testing infrastructure:

• With AI, you can understand the existing user behavior logs, code changes, or requirement documents to suggest or generate the test cases. For example, if you train your AI models on historical defect data, it can highlight high-risk areas and also propose targeted tests to resolve them.

• Almost all AI-powered tools use computer vision to interact with UI elements, similar to a human approach. This will help you identify buttons, text fields, and drop-downs depending on visual cues instead of brittle selectors.

• Using self-healing automation, you can ensure that even if an element's ID or location changes, the test script uses multiple locators and machine learning to find the updated element and continue the test execution process without any human involvement.

• AI modules will monitor the test logs, test results, and usage analytics to continuously improve accuracy and relevancy. Over a longer period of time, this approach will optimize the coverage, identify flaky tests, and predict future failures.

How To Integrate AI Testing

Excited about using AI testing for your enterprise apps? To help you get started, we have listed all the steps that you must follow for this process:

1. To begin with, you must analyze your existing testing practices and identify the bottlenecks. Depending on the data that you will gather, you must choose an AI tool which can smoothly integrate with your existing DevOps pipeline and support the tech stack of your company.

1. Now it is time to prepare the training data to ensure the model is customized depending on your requirements. For this process, you must feed historical test results, logs, user sessions, and bug reports to the AI engine. It is very important to remember that the higher quality of the data you are using for the training purposes, the higher will be the reliability of the overall test cases.

1. Now, as you have finished training the AI model, you can start the testing process by generating test cases using natural language processing or visual recordings. These test engines will also help you to automatically create the test from user flows to reduce the initial manual effort.

1. We also suggest the testers to configure the AI models so that they can understand the UI components using fallback strategies like Text, XPath, CSS, visual markers, and other locator strategies. As the application starts to evolve and scale, the test suite will adapt automatically to ensure proper locator strategies.

1. Finally, you must remember to use analytics dashboards so that you can track the failed tests, fixed issues, and test coverage. Depending on the results and metrics that you achieve here, you must continuously refine the testing models using new data from releases, bug logs, and usage patterns. 

Best Practices For Using AI Testing

Finally, let us divert our attention towards some of the best practices that you must consider using while implementing AI testing within your test environment. This is because with these practices, you can not only ensure the proper utilization of the testing capabilities but also directly impact the quality of the application that you are working on:

• Since the entire concept of testing is completely new to the entire industry, we would never recommend you to quickly migrate all your test cases to this process. Instead, the most sensible approach would be to choose a non-critical test and experiment on it. Depending on the results that you achieve here, you can scale your test cases accordingly.

• While implementing AI QA testing, it is also important to invest in real device testing, as they show the actual behavior of devices. If you want to avoid the hassle or expense of a physical device lab, we would recommend using cloud-based platforms like LambdaTest. LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.

• Since this is a rising practice, it is only relevant that new technologies and approaches will be introduced in the segment of AI testing. We would recommend the testers to constantly look out for all these new additions and incorporate them as soon as possible to further refine the experience for the users.

• Finally, you should always remember that even with such advancement, artificial intelligence can never completely eliminate the requirements for a human tester. Instead of implementing a substitution approach, it is recommended that you implement a methodology where artificial intelligence and human expertise work hand-in-hand to complement each other and perfect your testing cycle.

Apart from all the above practices, it is also important to understand that there will be certain parameters that need to be customized depending on your own needs or the specific requirements of the application that you're currently developing.

The Bottom Line

Based on all the areas that you went through in this article, you can easily come to the conclusion that AI testing is very important for enterprises who are trying to stay competitive with the changing and rising expectations of the users.

By using all the capabilities and advantages of using artificial intelligence and machine learning, the application developers can ensure that the QA teams can focus on what's truly important, including delivering a seamless and high-quality user experience to the customers.

Although like any other new trend, AI also has its own share of disadvantages, but by using a strategic planning approach and the best practices that we have given, you can easily ensure that it becomes one of your greatest advantages to provide an experience to the customers that was previously almost impossible to even imagine.