AI Mobile Testing Best Practices and Future Trends

Testers turn to AI for mobile app testing to navigate the complexity of modern apps with smarter, faster, and more reliable methods. AI tools can learn from data, predict outcomes, and automate tasks, which means they can efficiently handle the repetitive and intricate aspects of testing, allowing testers to focus on more strategic activities.

This blog will serve as your guide. We’ll uncover mobile app testing best practices, decode their significance, and take a look into possible future trends.

What is mobile testing?

First and foremost, we need to understand what mobile testing is, the different types of it, and the challenges faced by traditional testing methods.

Mobile testing is a procedure for evaluating the quality, functionality, usability, and consistency of mobile applications. The goal is to provide an optimal user experience by identifying and reporting potential issues before the application reaches the end-user.

Different types of mobile testing

There are several types of mobile testing, each addressing a specific aspect of the application.

1. Functional testing
   Functional testing verifies that the functions of the application work as expected. It involves checking the user interface, APIs, database, client/server communication, and other functional aspects.
2. Performance testing
   Performance testing evaluates the application’s behavior under certain conditions, such as low battery, bad network coverage, low available memory, and simultaneous access to the app’s server by several users.
3. Usability testing
   Usability testing checks to see if the app is easy to use and provides a satisfactory user experience. It involves testing the design and user interface of the application.
4. Compatibility testing
   This type of testing ensures that the application performs well in different environments, including various mobile devices, operating systems, screen sizes, and network conditions.
5. Security testing
   Security testing involves testing the application to identify any potential vulnerabilities or threats within the application. This is done to avoid any chance of putting the user’s data at risk.

Challenges associated with traditional mobile testing approaches

Now that we’ve covered the overview of different types of mobile testing, let’s look at the various challenges faced while approaching mobile testing with traditional approaches.

1. Time-consuming manual testing: Manual testing can be time-consuming and easy to cause human error. It also becomes increasingly complex with the growth of the application.
2. Frequent OS updates: Frequent updates in mobile operating systems require the apps to be tested repeatedly to ensure they work correctly with the new updates.
3. Device fragmentation: With a multitude of devices available on the market, each with different screen sizes, operating systems, and hardware capabilities, ensuring consistent performance is a significant challenge.
4. Localization issues: Ensuring the app functions correctly in different regions, considering the variations in languages, date-time formats, etc., can be challenging.

The importance of fusing AI in mobile app testing

The power of AI lies in its ability to learn, adapt, and improve over time, and the user using it will be able to bring out the highest potential of AI if it is used correctly.

The fusion of AI mobile testing is a significant advancement in the field of mobile software testing. Here’s why this fusion is so important:

1. ‍Reducing manual effort: AI can help you automate many aspects of the testing process, including test case generation, execution, and even analysis of the results. This not only reduces time and effort but also minimizes the risk of human error.
2. ‍Handling frequent OS updates: Rely on AI to create flexible test scripts that can adapt to changes in the operating system. This means that when an OS update occurs, the script can adjust the test cases accordingly.‍
3. Overcoming device fragmentation: Use AI-driven tools like BrowserStack or SauceLabs to perform testing across multiple devices and operating systems, all the while reducing the cost of purchasing and managing multiple real-life devices.
4. ‍Addressing localization issues: AI models, trained to understand different languages and cultural differences, play a crucial role in localization testing. These models can automatically validate the app’s UI, content, and functionality across different regions and languages.

Best practices for AI mobile app testing

Now let’s get down to the main section of this blog. By following these best practices, you can utilize AI in testing to get the most out of your efforts.

Automated test case generation

For mobile automation testing, AI can generate test cases based on your specific mobile app testing requirements and user behavior predictions. This involves training an AI model with your requirements and user interaction data, which it then uses to generate relevant test cases for your automation testing.

How to do it:

1. Define your requirements: Let’s say you want to test the checkout process of your e-commerce application. The requirements could be that the app should be able to add items to the cart, calculate the total price, apply discounts, handle payments, and confirm the order.
   
2. Explain requirements to AI: You would then explain these requirements to the AI model. This could be done through a natural language interface or by providing the AI with structured data that represents these requirements.
   
3. Generate Test Cases: The AI model, after understanding your requirements, can generate test cases. For example, a test case where it adds multiple items to the cart applies a discount code, proceeds to checkout, selects a payment method, and confirms the order.
   
4. Review the Results: After the AI has generated the test cases, you will review them to ensure they cover all the requirements. If the test cases are not satisfactory, you can provide feedback to the AI model.
   
5. Update the Request: Based on the review, you might need to update your request to the AI model. For instance, if the AI did not generate a test case for a specific payment method, you could update the request to include this requirement.
   
6. Repeat the Process: You will repeat this process until the AI model generates test cases that cover all your requirements. Over time, the AI model will learn from your feedback and generate more accurate test cases.

Predictive analysis

AI tools can predict potential problem areas in the application. This involves using AI for AI testing, where it analyses historical test data and identify patterns that might indicate potential issues.

How to do it:

Let’s consider an open-source tool like TensorFlow for predictive analysis in mobile testing.

• ‍Collect Historical Test Data: Gather historical data from your previous test runs. This data might include information about the test cases, the results of the tests, the configurations under which the tests were run, etc.‍
• Preprocess the Data: Clean and preprocess your data. This might involve handling missing values, normalizing numerical data, encoding categorical data, etc.
• ‍Train the AI Model: Use TensorFlow to create and train a machine learning model on your preprocessed data. The model will learn to recognize patterns in the data that might indicate potential problem areas in the application.

import tensorflow as tf
from tensorflow import keras

# Assume X_train is your preprocessed historical test data and y_train are the corresponding results
model = keras.Sequential([
    keras.layers.Dense(64, activation=tf.nn.relu, input_shape=(X_train.shape[1],)),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(1)
])

model.compile(optimizer=tf.optimizers.Adam(), 
              loss='mean_squared_error',
              metrics=['mean_absolute_error', 'mean_squared_error'])

model.fit(X_train, y_train, epochs=50)

• ‍Predict Potential Problem Areas: Once the model is trained, you can use it to predict potential problem areas in new test data.

# Assume X_test is your new test data
predictions = model.predict(X_test)

• ‍‍Analyze the Predictions: Analyze the predictions to identify potential problem areas in the application. You might need to set a threshold to decide which areas are likely to be problematic.

Real-time reporting

AI can provide real-time updates about the testing process. This involves using AI-driven reporting tools to analyze test results in real time and provide instant feedback.

How to do it:

1. Sign up for Testim.io: Start by signing up for Testim.io. They offer a free trial, so you can test out their features before committing to a subscription.
   
2. Integrate Testim.io with the framework: Testim.io provides easy integration with popular testing frameworks. Follow the instructions provided in their documentation to integrate Testim.io with your existing testing setup.
   
3. Run your tests: Once Testim.io is integrated with your testing framework, run your tests as you normally would. Testim.io will automatically track and analyze your test results.
   
4. Review the AI-driven insights: You can view these insights on the Testim.io dashboard. This includes information about test pass/fail rates, test flakiness, slow-running tests, and more.

Self-healing tests

One of the most powerful features of AI in test maintenance is the ability to create self-healing tests. These are tests that can automatically update themselves when changes are detected in the application.

How to do it:

Use tools like Testim.io or Mabl, which offer self-healing test capabilities. They use machine learning algorithms to understand the structure and elements of your application, allowing them to adapt to changes and keep your tests up-to-date.

Remember, the key to successfully integrating AI into your testing efforts is to understand the capabilities of AI and choose the right tools that fit your specific needs.

Intelligent test distribution

AI can enhance parallel execution by intelligently distributing tests based on various factors, such as their past execution time, complexity, and failure rate. This ensures optimal utilization of resources and can lead to a significant reduction in overall testing time.

How to do it:

Implement AI-driven mobile testing tools (which vary based on your requirements) that offer intelligent test distribution. These tools use machine learning algorithms to analyze historical test data and distribute the tests in a way that optimizes the testing process.

AI-driven usability testing

AI can significantly enhance usability testing by analyzing user interactions and providing insights into the user experience. This involves using AI to evaluate how users interact with the application and identify areas where the user experience can be improved.

How to do it:

Use open-source usability testing tools in your testing process and rely on their AI power to provide insights into user behavior, helping you improve the usability of your app.

These tools can analyze user interactions with the app and provide insights into the user experience, helping you identify areas for improvement.

Key points to know before using AI for mobile testing

There are several myths and points every quality enthusiast must know before leveraging AI into the testing process.

• AI is a Tool, Not a replacement: While AI can perform automation testing and enhance many aspects of testing, it doesn’t replace the need for skilled testers.
  
• Learning and Adapting: The AI is not perfect, it is learning and evolving with time.
  
• Right tools for the right job: Not all AI tools are created equally. Different tools have different strengths and are suited to different tasks.
  
• Human verification: Requesting and generating test cases or scenarios from AI alone is not enough, testers have to intervene and combine their expertise with AI to bring out the best-expected result possible.

Future of mobile app testing

As we are reaching the end of the blog, let’s take a look at our predictions for future trends and innovations in mobile testing.

Blockchain-based testing 

With the rise of decentralized apps (dApps), there’s a need for specialized testing methodologies. Blockchain-based testing will ensure the integrity, security, and functionality of these dApps.

Quantum computing

As quantum computing becomes more accessible, it could revolutionize mobile testing. Quantum algorithms can potentially process vast amounts of data and complex computations faster, making it possible to test every possible user interaction and scenario.

Conclusion

In summary, we navigated through mobile testing, its challenges, and the role of AI in overcoming them. We discussed best practices for integrating AI into mobile app testing services and key points to consider before implementation. We also explored future trends like blockchain testing and quantum computing.

If you're interested in AI and mobile testing, and how it can benefit your app's development process, consider partnering with us, a Mobile app testing service provider that uses AI-powered tools. Additionally, check out our blog about different AI-driven tools for mobile automation testing.