Starting to use AI in your applications? You must have come across a common challenge which involves testing the proper functioning of all the AI features within your application. This is because if you do not perform this process, your applications will have unwanted complexity, some unpredictable behavior, and dependency on multiple third-party factors. So, if you test AI features in your application, you will be ensuring that you are providing the intended experience to your customers.
Don’t know how to test AI applications for reliability? Don’t worry! We are here for you. Our article will help you understand all the effective strategies to test AI applications. We will also help you with various methodologies, the tools, and best practices that can further improve the efficiency of this process.
Why AI Apps Can Be Unreliable
If we are in the context of AI app reliability, we are mainly referring to the app’s performance being consistent and accurate under multiple varying factors and over a longer period of time. The application’s reliability also includes its robustness, accuracy, performance, and resilience to multiple forms of unexpected user interactions.
When it comes to implementing reliability on web applications with AI features, the testers will face the following challenges:
- AI systems often have unknown deterministic behavior, which makes traditional testing methods insufficient to verify the proper functioning, interactivity, and placement of all these elements.
- All the modern artificial intelligence models work with high-dimensional data. Therefore, it can be a very complicated task to create accurate test cases manually to verify the proper functioning of these models.
- If your AI system is using the same training data over a longer period of time, it is highly possible that it will develop some form of bias. Ensuring the reliability of the testing infrastructure with these biases in data and model prediction algorithms can be a complex process.
- Since the entire concept of AI in software development and testing is an evolving practice, all the new inclusions and integrations can add further complexity while you are trying to establish the reliability of the workflow.
See also: How Biotechnology and AI Are Merging
Tools For AI Reliability Testing
While you’re trying to verify the reliability of your AI application, you can use various tools available in the market to assist you in this process. To shed more light over this segment, we have mentioned some of the tools for this purpose:
TensorFlow
The TensorFlow testing framework will provide you with multiple individual tools that can help you verify the functioning of your AI model. A significant downside of using this framework is that it can only verify the functioning of applications and web elements that have been designed using the TensorFlow-based AI model.
PyTest
If you’re trying to verify the integration of your AI features and the system-level stability, you can use various tools like PyTest. Using this tool, you can also perform AI-driven user interface testing to ensure that all the UI elements are properly placed.
LambdaTest
While you are trying to introduce AI in software testing, you must remember that it is equally important to verify the functioning of the AI elements on the physical parameters of your device. This is because all these elements can function differently when exposed to a broken display, a low battery, or any form of user interaction error.
But how do you perform this process while avoiding the millions of dollars that are usually required to set up an on-site device lab? It’s very simple! You start integrating AI cloud platforms like LambdaTest with the test bench.
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.
After running all the test cases, LambdaTest will also provide you with a very detailed test report with multiple media elements like screenshots and videos. The purpose? To quickly help you find all the faulty elements and implement the required debugging steps.
Strategies for AI Reliability Testing
Excited about testing the reliability of your AI applications? To further help you with this process, we mentioned some of the most effective strategies that you can follow in this process:
1. Functional Testing
You can use functional testing to verify whether the AI application meets the specifications that you have previously set for your app. This step is highly important to ensure expected deliveries and prevent any unwanted surprises after the deployment.
While implementing functional testing on your application, you need to perform the following test cases as a part of it:
With unit testing, you will be able to verify the specific component functioning or the modules to ensure that each of these elements are working functionally and properly on their own.
With integration testing, you can validate that when different AI models, elements, or systems are attached together, they are functioning properly. This is a very important parameter to ensure the overall stability of your app.
Finally, with system testing, you can ensure the complete AI system performs as expected under realistic conditions. This is a very important parameter, as you cannot foresee what integration methods or interaction systems your users will be using on these apps.
2. Performance Testing
Using performance testing, you can understand how the AI applications are functioning under different workloads. This is a very important step to create a performance benchmark that will be efficient in measuring the stability and accuracy of the application infrastructure.
Stress testing is a very important part of performance test cases as it will push the AI system to its operational limits. During this step, you will be able to understand the robustness and stability of the entire infrastructure.
Using load testing, you can understand how the system behaves under unexpected user volumes. This will be a very important feature to guarantee that even if you have a sudden surge of users, the application does not crash completely.
Finally, using endurance testing, you can understand the stability of the AI system over an extended period of time. In simple terms, this step will help you prevent any form of downtime within your application infrastructure.
3. Robustness Testing
Using robustness testing, you can understand how the AI model remains reliable when exposed to unexpected or adversarial inputs, often done by the customers or end users.
Adversarial testing is a process of adding deliberate challenging models with adversarial examples to ensure the resilience and stability of the working infrastructure.
Using mutation testing, you can slightly change the inputs to understand the sensitivity of the model and also the stability of the response based on these changes.
Finally, fault injection is a process of introducing errors to understand the AI system’s error handling capabilities if such a thing were to happen in reality.
Best Practices for Reliable AI Application Testing
Finally, we strongly advise you to add the following best practices with your AI app testing cycle to ensure the reliability, productivity, and efficiency of the infrastructure. These practices will also have a definite role in improving the quality of the application that you’re currently working on:
1. Adopt Continuous Testing
You must remember that the process of integrating AI with your software is a continuous process. So, the testing steps should be continuous as well. You can also consider using continuous deployment and continuous integration pipelines so that you can test all the AI models with new data and scenarios.
This approach will also be very useful to quickly find the errors in the application’s source code before they can become a serious concern to the core functioning.
2. Automated Monitoring and Alerts
We also advise the testers to start investing in automated monitoring systems. These systems will help you to find and respond to performance degradation or unexpected hiccups immediately.
This means you can ensure that any minor bug or AI integration error does not completely crash the main architecture of your application.
3. Document Test Cases and Results
While you are testing the functioning of the AI elements, you should be very careful to maintain comprehensive documentation. This approach will be very important to ensure transparency and also help you reproduce the bugs while debugging them.
This documentation will also serve as a reference point so that you can avoid all previously known errors in future iterations of your application.
4. Multi-disciplinary Teams
While working with artificial intelligence and machine learning, it is a very good approach to involve multiple teams, like data scientists, engineers, domain experts, and enthusiasts. With this approach, you will be able to cover all the aspects of reliability and also maintain updated information about your current status of the AI testing process.
5. Regulatory Compliance
Whenever you’re working with artificial intelligence, there will be a widespread concern about using sensitive user or organization information to train the AI models. To avoid any legal complications, you should ensure that you are meeting all the regulatory requirements and standards depending on the domain of your application.
6. Start Small
Since the entire concept of artificial intelligence in software testing is completely new, you should not straight away migrate your entire testing infrastructure through this process. So what’s the best way? You should start small with a non-crucial test case within the environment.
After the implementation process, we will closely monitor the key KPIs of the test reports to verify whether it was a successful and effective transition. Depending on the reports that you receive in this step, you can truly scale and start integrating AI in the other areas as well. This approach will also be useful to gain the trust of the stakeholders who might be initially skeptical about this method.
The Bottom Line
Based on all the areas that we have covered in this article, we can easily come to the conclusion that testing AI apps for reliability will cover various comprehensive and multi-layered strategies to ensure the accuracy, performance, robustness, fairness, and explainability of your entire testing environment.
However, by using the strategies and best practices that we provided in this article, you can easily perform this process to ensure that your test cases are not only safe to deploy but are also providing the best end-user experience to your customers. Factors like these will have a huge impact on your brand reputation and will also help you to constantly expand your target audience.
Moreover, as AI starts to advance and capture more of the software market, practices like these will be very important to enhance and maintain the trust and effectiveness of technology.
