Organizations that adopt artificial intelligence (AI) in testing of microservices-based applications gain better accuracy, faster results, and greater operational efficiency.
AI and machine-learning technologies have matured over the last few years, and today their application in automated testing can help in more ways than one. In fact, AI has redefined the way microservices-based applications are tested, especially when it comes to canary testing.
The introduction of AI in software testing helps both developers and testers alike. It improves accuracy; the same steps can be performed accurately every time they're needed. Automated testing can increase both the depth and scope of your tests, resulting in more thorough overall test coverage. You can also leverage AI to simulate a large number of users interacting with your application.
Here's how AI-enabled automation can help you test as you scale microservices-based applications, as well as the challenges you'll face and effective strategies you can adopt to overcome them.
Why traditional testing strategies don't work
Traditionally, when creating monolithic applications, you'd test each unit of code with unit tests. As different components of the application are joined together, you typically test your application using integration testing first and, usually, system testing, regression testing, and user acceptance testing follow.
If the code passes all of these tests, the release goes out.
Testing microservices-based applications is not an easy task and is not the same as testing monoliths; you must be aware of not only the service you are testing but also its dependencies—the services that work with the services under test.
Owing to the granular nature of microservices architecture, boundaries that were previously hidden in a traditional application are exposed. You might have several different teams spread across geographical distances working simultaneously on different services; this makes coordination extremely challenging. It can be difficult to find a particular time window to perform end-to-end testing of the application as a whole.
The distributed nature of microservices-based development poses many challenges to testing your application. These include:
- Availability: Because of the distributed nature of microservices architecture, it is difficult to find a time when all microservices are available.
- Isolation: Microservices are designed to work in isolation together with other loosely coupled services. This implies that you should be able to test every component in isolation as well as testing them together.
- Knowledge gap: You should possess a strong knowledge of each microservice; this would help you to write effective test cases.
- Data: Each microservice can have its own copy of data. In other words, each can have its own copy of the database, which may be different from another microservice's copy. As a result, data integrity poses a challenge.
- Transactionality: Unlike with a monolith, where transactionality is often assured at the database level, implementing transactionality between different microservices is challenging, because a transaction can consist of various service calls spread across different servers.
Typically, a microservices-based application consists of several services, each of which can dynamically scale up if needed. There is also a risk of failure and the cost of fixing bugs or issues after integration. Hence, you should have an effective test strategy in place for testing microservices-based applications.
How to build an effective testing strategy
To build an automated testing process for a microservices-based application, you should follow the same best practices you would for any other type of testing:
- Understand the customer's expectations as far as test automation is concerned.
- Set quality goals—and adhere to them.
- Analyze the testing types that are right for you to achieve the goals.
- Write tests according to the test pyramid (i.e., considering that the cost of the tests increases as you move up the pyramid).
AI-driven test automation: Embrace innovation
Today's software testers can take advantage of AI for test creation, test execution, and data analysis by using natural-language processing and advanced modeling techniques. AI-based sofware testing can help by increasing efficiency, facilitating faster releases, improving test accuracy and coverage, and allowing for easier test maintenance, particularly when it comes to managing your test data.
For efficient test maintenance, you need to know what is happening to your data at the time of test creation. Inadequate data modeling is one reason why test maintenance fails, becoming a bottleneck in your deployment pipeline. AI can help with efficient data modeling and with root-cause analysis.
Repeating tests manually each time the source code changes can be time-consuming and costly. Once you create automated tests, you execute them repeatedly and quickly with no additional cost.
Use AI for canary testing
Canary testing helps reduce risks by gradually rolling out the changes to a small group of users before presenting it to a larger audience—and it is particularly useful in the testing of microservices-based applications. In a typical application, the changes to microservices happen independently of one another, so those microservices need to be verified independently as well.
AI can help automate canary testing of microservices-based applications. You can take advantage of AI concepts such as deep learning to identify the changes in the new code and the issues within it. AI can be used to compare the experience of the small group of users with that of the existing users, and this can be done automatically; you don't need any human intervention in the loop.
Challenges in AI-based microservices testing
AI-based testing does have some constraints. While you can automate functional and unit tests, it is quite difficult to automate integration tests, because of complexity.
Some of the other challenges in AI-based testing include the following:
Skills
Testing microservices-based applications with an AI-based approach requires extensive technical expertise from testers, and is very different from what manual or automation testers are used to. Testers should be adept at how to use AI-based tools specifically for microservices-based applications.
Use cases
Learn how to determine the best use cases for using AI in microservices test automation. One is to use AI for creating your unit tests. You can take advantage of AI to perform static code analysis and determine the portions of code that are not covered by your unit tests.
You can also use AI to update unit tests as soon as the source code changes, as well as for test creation, execution, data analysis, and API testing in microservices-based applications.
AI can help you understand the patterns and relationships in API calls and come up with more advanced patterns and inputs for testing the API. You can leverage an AI-powered continuous testing process to more efficiently detect the controls that have changed.
AI-based testing can't do everything
AI-based test automation of microservices can create more reliable tests, and in so doing slash the time needed for test creation, maintenance, and analysis. Such tests can in turn be used to check the service-to-service communication, test communication paths, etc.
You can also leverage deep-learning models and other AI techniques to empower your team to build tests faster and execute them at scale in the cloud.
Adopting AI for microservices test automation is no panacea. It won't magically eliminate all problems associated with software testing. But it can help you make your software testing process smarter, more efficient, and faster—and thereby deliver business value consistently.
Keep learning
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