Have you come across a modern app without the slightest use of AI? No? This is because modern apps have been proactively using artificial intelligence capabilities to bring in various innovative and productive features. These features can range from recommendation engines and chatbots to advanced computer version systems. This has also made total AI E2E testing an equally important part of the app development life cycle.
Traditional testing processes like unit integration and system testing will still play an important role but cannot verify the functioning of AI-based systems. This is because these advanced systems have data-driven models and a problematic nature, along with a continuously evolving code base. Therefore, you need additional robust features to verify the proper functioning of all these new additions.
What Are The Challenges Of AI E2E Testing
Before we dive into the details of implementing AI E2E testing, we must have a thorough understanding of all the challenges that we will face during this phase. If you work with traditional software testing, you will see that it has a deterministic approach where the system uses a specific input to create a predictable output. On the opposite side, AI-driven components often use problematic or approximate behaviors that cannot be verified with specific outputs.
To shed more light on this segment, we have mentioned the immediate challenges of using AI E2E testing in the modern application development and testing life cycle:
- A machine learning model will generate different outputs for the same input depending on various factors like data drifts, randomness in weight initialization, or outputs in model parameters. Therefore, it will never be deterministic at any phase of the testing process.
- Machine learning models will be massively dependent on the quality of the data that you are training them upon. This data should also be relevant and distributed equally in terms of the input data. Even a minor change to these datasets can cause significant shifts in model performance.
- All the modern AI systems, especially the ones with deep learning capabilities, will have millions or even billions of separate parameters. Therefore it will be massively difficult to interpret the behavior and diagnose these issues when working with huge parameter variations.
- You will see that all the modern AI systems are frequently updated with new training data or refined model architectures. Therefore, this continuous evolution will also require testing frameworks that can handle these frequent changes and keep up with them.
- Considering the modern testing requirements, the AI models will rarely work on isolated units. Instead, they will integrate with front-end interfaces, back-end interfaces, APIs, databases, and even more. So, evaluating the end-to-end functioning of such a structure will require a complex web of interactions.
Understanding all these challenges is equally important as they will help you to design an advanced E2E testing pipeline that can adapt to all the complexities of using AI in the modern application development life cycle.
Test Strategy and Framework Selection
It is very important to have a well-defined strategy to ensure the proper execution of advanced AI E2E testing. This strategy will be dependent on the nature of the solution, like is it an NLP-powered chatbot, oecommendation system, or image classification model? Each of these domains will have a separate requirement and thus will require different strategies as well:
- You must begin the process by determining whether your tests are primarily checking end-to-end user behaviors or more technical functions like correcting data format and stable codes.
- Since AI models can fail in very minor ways, you have to work towards striking a balance between broad test coverage and deep test analysis. Factors like these are crucial to ensure the proper performance of the AI model against tricky edge cases.
- After this, you have to select your preferred framework for executing the AI test cases. Some of the common options include Pytest, Selenium, Cypress, or Kuler Flow. In this case, we would also remind the testers that this selection will also vary depending on the goals that you’re trying to achieve.
- You can also use cloud platforms like LambdaTest to execute AI-based real-device and web testing. LambdaTest is an AI-powered test orchestration and execution platform that lets you perform manual and automation tests at scale with over 3000+ real devices, browsers, and OS combinations. With this platform, you can also perform AI test automation using the kaneAI tool that lets you perform AI E2E testing across a wide range of testing environments seamlessly.
- The final step in this process is to ensure that data scientists and QA engineers work in close collaboration to define your success criteria and also other relevant metrics. By using this cross-functional approach, you can ensure that the entire test suite includes various diverse parameters like e-mail-specific checks, distribution, and verification, or drift detection and fairness tests.
Data Preparation and Management
Since data is the heart of all forms of AI testing, you must use E2E testing capabilities to validate not only the model’s code but also the correctness of these data. To shed more light on this segment, we have mentioned some of the crucial parameters that you can consider in this process:
- You must begin this process by using version control for data sets to track changes over time. You can also consider maintaining metadata about how data was obtained, processed, and fed into the same model.
- In certain cases, you might not have access to real-world data or that data might be restricted due to privacy and other compliances. In such a scenario, you will have to use synthetic data generation to fill the gaps, simulate rare corner cases, or test how the system handles out-of-distribution samples.
- Now comes the most important part: data validation. Here, you have to check if the data conforms to expected formals, feature types, and ranges. You also have to compare the distribution across different data slices. You can consider using automated alerts to find significant deviations that might indicate data corruption or drift.
- We suggest the testers tut in efforts to ensure that training and inference data follow the same transformation and preprocessing steps. To implement this, you can use automated checks, which will find a mismatch between training and production data pipelines at the earlier phases of the development cycle.
- The final step in this process is to ensure that your AI model maintains the required amount of data security and data privacy. To perform this process you must incorporate security scanning tools for finding exposure or personal information in logs or data outputs. It is also a great idea to use anonymization or data encryption when you’re transferring the data for testing in a non-production environment.
Performance, Scalability, and Reliability Testing
Using AI capabilities in an application can be resource-intensive. At the same time, user-facing AI services will often have strict latency requirements. So, to prevent any mishap due to these challenges, you should implement rigorous E2E tests for scalability, performance, and reliability:
- To properly input low latency in the application, you have to measure how long interference takes and how many requests per second the system is handling. In this regard, you should also identify bottlenecks like GPU constraints and network overheads to ensure the system meets the minimum SLA requirements.
- To implement proper load testing, you can consider increasing the number of concurrent requests to see if the system can scale horizontally or vertically as per your requirements. During these load handlings, you should also understand how much CPU, GPU, and memory the system is using. You should especially watch out for queue buildups or timeout requests.
- To implement failure testing, you should use chaos engineering principles, which will randomly shut down resources or inject latency errors to understand how the system responds to these. You should also monitor the system’s recovery process and whether the AI component can handle partial data or incomplete requests.
- The final step in this process is to implement resource optimization to ensure the system is efficient by running smaller and optimized models and using hardware accelerators selectively. You should also cover these configurations in the testing cycle to prevent surprises during the deployment process.
Monitoring and Feedback Loops
The final step in this process is to constantly monitor the AI E2E testing process and implement feedback to further improve its functioning and productivity. To shed more light on this segment, we have mentioned the parameters below:
- Begin by tracking metrics like response time, error rates, and hardware utilization. You should also log all predictions and optionally store them for future analysis and also keep track of known errors.
- We suggest the testers configure alerts for anomalies within the model performance, like sudden drops in accuracy or spikes in the error rates. You can also consider using tools like Prometheus and Grafana that can help you visualize the real-time telemetry to handle these triggers appropriately.
- It is a good idea to deploy in-app ratings for recommendations or chat about satisfaction codes. All these approaches will help you collect end-user feedback about how the AI model is performing and whether they want any changes in this regard.
- The final step in this process is to test new models on live traffic in parallel but without affecting the production outputs or the deployed version of the application. You should also compare predictions to the live system by gathering performance metrics before fully releasing the application.
The Bottom Line
Based on all the factors that we have put forward in this article, we can safely say that building AI testing frameworks for modern applications is completely different compared to traditional testing practices. A well-designed AI pipeline will include stages for thorough data validation, model verification, integration testing, performance benchmarking, and even continuous monitoring.
We should also remember that all these pipelines will not be static. This is because, with the inclusion of new tools and technologies, like data drift detection or synthetic data generation, the app structure will continue to change as well. Therefore, your organization will only succeed if you can create a culture of continuous improvement and learning.
As an AI-based software tester, your final goal should be to create an AI lifecycle that is tested, validated, and refined at the same time. By creating this robust pipeline, you can use the power of AI while avoiding risks and delivering reliable and high-quality software to your prospective customers.
