You must have heard about test automation, right? It has completely changed how we see and execute modern testing processes. But truth be told, simple automation testing isn’t enough to verify the performance of modern artificial intelligence-driven applications. So what’s the solution? You need to implement AI test automation especially while working on large and enterprise environments.
In this blog, you will learn what is AI test automation for enterprises you will also learn various best practices, frameworks, challenges, and strategies for implementation.
Why AI Test Automation Matters?
Before we go into all the complex details, let us turn back and understand why you should bother about AI test automation in the first place. Although automation testing has been massively improving the testing cycles with continuous feedback, consistent results, and agility, artificial intelligence brings new complexities to the app development and testing realm.
This is because AI-based applications are heavily dependent on machine learning algorithms, huge datasets, and probabilistic inference that is completely separate from deterministic code paths. This means that you cannot use traditional rule-based automation in this regard, as it will struggle with the inherent changes and fluidity of AI systems.
Moreover, if you’re working in an enterprise environment, this challenge will become even more massive with the inclusion of huge operations, regulatory requirements, and complex technical systems. This is where AI test automation comes to the rescue by improving the speed and reliability of the process to reduce manual errors and also free up human time to focus on other important areas like exploratory testing.
Although AI test automation is still in the early phases, it is progressing rapidly. Just a few years ago, you could only use artificial intelligence to create test cases or develop self-healing test scenarios. Now, AI has come to such a stage that it can optimize your testing strategies, understand the testing risks, and also validate itself against all modern techniques like metamorphic testing.
Primary Principles of AI Test Automation
Before we start discussing all the implementation steps for AI-based automation, let us go through some of the major principles that form a crucial part of this entire infrastructure:
- Data-Centric Testing: You must remember that while you’re working with artificial intelligence, data is your most prized possession. All machine learning models will depend on this data to create everything related to the testing process. To implement a proper data strategy, you can consider including various strategies like data labeling, data versioning, and data security compliances.
- Model-Centric Testing: Although traditional testing practices force you to focus on the lines of code, AI automation maintains its focus on the entire model. This model will consist of both the training and inferior behaviors.
While implementing this practice, you must use model drift detection, bias, and fairness analysis along with performance and scalability implementation. Bias and fairness analysis will be very important as, over a period of time, the machine learning model will have special preferences based on the training data. So, this approach will help you evaluate such potential biases by systematically comparing outcomes with demographic subgroups.
- Continuous Integration/Continuous Delivery (CI/CD) Alignment: You must integrate the artificial intelligence testing framework within the existing development life cycle. This process will primarily help you to execute the development and testing processes at the same time. In the ideal scenario, you trigger the test evaluation process, performance, and functional testing automatically with every code or data commit.
The proper implementation of this approach will ensure that the developers and testers receive timely updates about the current status and functioning of the application infrastructure.
- Self-Healing Test Scripts: While working with complex AI applications, the user interface will rapidly change depending on various factors, including user interactions. The self-healing test scripts will use artificial intelligence algorithms to adjust according to minor changes like updated user interface locators or new design changes.
The implementation of the above process will help you reduce the maintenance overhead while also helping the quality assurance teams maintain stable regression testing cycles.
AI Test Automation Implementation
To help you understand the workflow of implementing AI test automation, we have mentioned all the steps that you must follow in the correct order:
- Assessment and Planning
You have to initiate this process by understanding your current quality assurance workflow, test coverage, and also the organization’s readiness to implement this approach. It is equally important to set clear expectations for this process, like what you want to achieve with AI test automation? Your goals can include reducing test cycle time or improving the accuracy in directing the model drift.
Finally, in this step, you also have to find all the key stakeholders who must be informed about each and every function that will be performed in this workflow.
- Data Preparation and Governance
Proceed towards working closely with data engineering teams to ensure reliable data pipelines. In this step, you must create and refine your data governance policy to specify how data is collected, stored, labeled, and versioned.
You should also ensure that the automation tasks include data quality checks for variables like missing values, duplicate values, or outliner values. You should also have special tests for implementing compliance triggers.
- Selecting the Right Tools
The next step in this process is to find the right tools for the environment, the existing QA framework, and your level of AI maturity. You must be careful that you are choosing only those tools that work closely with your frameworks and particular needs.
For example, if your main concern is user interface testing, you must consider using solutions at Test.AI. On the other hand, if you want to implement MLops coverage, you can go with KuberFlow to integrate continuous testing into the modern life cycle.
- Building the Test Automation Architecture
Now that you have finished all the prerequisites, you are completely prepared to create the test automation architecture that you can integrate with your continuous integration and continuous deployment pipeline. You can begin this process by implementing model training, validation, and deployment triggers. These triggers must be directly incorporated within the automated testing pipelines.
It is also a good approach to have a dedicated environment that can mirror the production conditions, including the container orchestration and microservice configurations that have already been developed.
- Writing AI-Centric Test Cases
Now, you have to start creating and organizing the test cases that are compliant with your artificial intelligence use cases. Some of the test cases that you can consider for this step include data-driven tests, model performance tests, integration and regression tests, and bias testing.
In summary, all the tests that are repetitive and can also give a better understanding of the current user parameters and real-world usages will be good candidates for AI test automation.
- Execution and Reporting
After your automation test scripts are ready, you can integrate them within the CI/CD pipelines. The system that you will be creating for AI test automation must include the following:
- Ability to call the latest model artifact from a registry like MLflow.
- Capabilities to run necessary training or inference steps within the datasets for interaction and execution.
- Execution commands for all relevant test suites, including bias, performance, regression, and security checks.
- Production capabilities to create detailed test reports with actionable insights that can be later used for test debugging and troubleshooting.
- Monitoring and Continuous Improvement
The final step in this process is to monitor the AI application using the AIops tool. While monitoring, you must keep track of any anomalies like sudden drops in accuracy or performance. Then, you have to refeed these triggers into the test automation pipeline so it can learn from previous mistakes.
Over the passing time, your goal should be to refine the test cases for addressing new cases, model updates, or data shifts. Continuous improvement is a very important part of the implementation process as it ensures that the entire structure remains relevant, scalable, and robust.
Tools And Frameworks For AI Implementation
While implementing AI automation testing, you can utilize multiple tools and frameworks to further improve the productivity and efficiency of the testing cycle. To shed more light on this segment, we’ve mentioned some of the major tools that are available in the market:
- You can use traditional tools like Selenium and Appium that allow you to integrate AI capabilities by using third party integrations and libraries. The major benefit of this approach is that you do not have to go through the learning curve of a separate tool just to use the artificial intelligence and machine learning capabilities.
- If you want to invest extra time and effort into AI-focused testing, you can utilize various tools like Test AI and Applitool AI. All these tools have dedicated AI features like visual recognition, natural language processing, and automatic test case generation.
- There are also multiple tools that allow you to implement AI testing. You can use a cloud platform that lets you perform real device testing by providing test infrastructure, and you are also able to scale your testing environment across various real devices and OS combinations. One such platform 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. It also allows you to implement AI E2E testing using the KaneAI testing tool.
- To implement machine learning platforms, you can consider using KuberFlow, MlFlow, and Data Robot. All of these tools will help you implement end-to-end machine learning workflows to manage multiple data models and also trigger automated testing steps.
- Finally, you can use artificial intelligence based continuous test orchestration by using tools like Jenkins or GitLab CI and Azure DevOps.
The Bottom Line
Based on all the factors that we have put forward in this article, we can safely say that AI test automation is very important to conquer modern dynamic technological requirements. By using all the strategies that er have mentioned here, you can dramatically improve the quality and accuracy of your QA processes.
The most important part of the adoption process is to start small, secured by informed stakeholders, and also expand depending on the success that you have already demonstrated.
You should also remember that with the passing years, automation testing will become even more autonomous as it will receive support from multiple frameworks and tools. So, it is important to continuously adopt all the new practices and trends in this regard to provide the best experience and applications to your customers.
