Is automating microservices testing really complex? Let’s have a look.
Keeping up with changing market trends and industry dynamics has become highly challenging for organizations – but that’s where microservices help drive unmatched growth, revenue, and engagement. Microservices offer the ability to quickly respond to changes and allow new features and experiences to be delivered almost instantly.
But despite helping stand up to new opportunities, microservice architecture also exposes unique challenges for responsible, quality-oriented testing teams, especially when it comes to automated testing.
So, what is microservices testing after all? And what makes automating microservices testing so complex?
Let’s explore –
What is microservices testing
Unlike monolithic architecture, a microservices architecture comprises a collection of services, each working independently and relying on APIs to communicate with other services. Since each microservice has its own data and is deployed independently, testing it requires a strategy that accounts not just for individual services but also the entire application as a whole.
Microservices testing involves testing each microservice in isolation as well as together with other services. Since the underlying architecture of microservices influences what techniques are available for decoupling a microservice from its dependencies for testing purposes, microservices testing is slightly more complicated than testing monolithic applications. Teams need to have a deep understanding of the complexities of microservices testing. They also need to embrace the right tools and best practices for desired testing outcomes.
The Inadequacy of Traditional Testing Approaches
The complex architecture of microservices makes traditional testing strategies insufficient for quality outcomes.
Here’s why the traditional testing approach is not enough while testing microservices:
- In monolithic applications, each code unit is tested via unit tests and then via integration tests when different components are joined. On the other hand, in microservices testing, testers need to be aware of the service they are testing and its many dependencies.
- Although microservices are designed to work in an isolated manner, they also need to work in association with other loosely coupled services – requiring testers to test each component in isolation and together as a whole.
If bugs end up being detected after integration, the risk of failure and the cost of fixing those bugs is exceptionally high.
- Because microservices are so granular, limitations that are usually hidden in a traditional application are highly exposed. Testers need to be extremely well-versed with these limitations and build testing strategies that align with the granularity.
- Since different teams work on various services simultaneously, performing end-to-end testing of the entire application as a whole – primarily when teams are spread across other locations is highly challenging.
The Need for Automation Tests for Microservices
Automation tests are critical to any successful microservices strategy. For example, if you want to deliver features and changes frequently, automation testing allows teams to achieve this – while gaining better accuracy, faster results, and greater operational efficiency.
Leveraging maturing AI and machine learning technologies, automating testing microservices helps developers and testers alike. Since complex testing tasks can be performed accurately every time, automation helps in improving both the depth and scope of microservices testing – leading to better test quality and coverage.
Not just that, automating microservices testing also helps in scaling applications. Furthermore, using automation, testers can quickly and easily simulate many users, thus improving the quality of the experiences for users while interacting with the application.
Building an Effective Automation Testing Strategy for Microservices
Although having an automated test strategy for your microservices is critical to the long-term success of your application, automating microservices testing is not easy.
Understanding user expectations, setting the right quality goals, analyzing (and then choosing) the right automated testing tools, and carrying out the tests requires detailed planning.
- For efficient microservices testing, each application needs to undergo a series of testing including unit testing, contract testing, integration testing, end-to-end testing, and functional testing. While automation is great for unit and functional tests, automating the other tests is not as easy, mainly because of dependencies.
- As the number of services or dependencies increases, automating tests to handle issues such as database errors, network congestion, latency, and service unavailability can get complex.
- Testers need extensive technical expertise for automating this test, which is often lacking in most traditional testing teams.
- Automating microservices testing means automation at several levels – unit, component, service, API, application, and others.
- Automation makes identifying errors a mammoth task. Understanding which component or service has a bug or failed business logic via automation is exceptionally challenging.
- Test automation also makes visibility into the testing process a considerable problem. As different teams use different automation strategies, getting insights into testing progress or quality can become difficult.
The Many Benefits of Using AI-Powered Test Automation Platforms
If you want to automate microservices testing, you need to take advantage of AI-based platforms for codeless test automation. Such platforms can help you streamline the test creation and execution process. Also, pave the way for advanced data analysis via modern modeling techniques. They can also help you detect and correct bottlenecks in your deployment pipeline. Meanwhile, allowing you to carry out detailed root-cause analysis to improve the testing process.
Intelligent test planning and change management helps increase efficiency, facilitates faster releases, improves test accuracy and coverage, and makes test maintenance easier. Tests are carried out automatically each time a change is made. And moreover, the AI-based automation testing platforms significantly reduce the time, effort, and cost of testing complex microservices applications. With such tools, you can identify not only changes made in the underlying code but also the bugs and issues within it.
You can use AI to perform static code analysis while also determining sections of code not covered by unit tests. What’s more, you can use AI to update unit tests as soon as the source code changes, as well as for test creation, execution, and data analysis. AI can help you understand patterns and relationships in your APIs. And moreover, test your microservices applications and their dependencies quickly and efficiently.
If the question “how to test microservices effectively” constantly plagues you, integrating AI-powered test management platforms as part of your microservices automation testing strategies is a great way to improve both the speed and accuracy of testing microservices. However, do remember that adopting AI for microservices test automation is not a magic wand to eliminate all problems associated with microservices testing. Still, the right approach, tools, and guidance can surely make your testing process more intelligent, more efficient, and faster. Also, helps deliver business value consistently!