How AI improves the automation testing of microservices.

Posted By :Sanjana Singh |23rd February 2021

Artificial Intelligence (AI) and Learning Equipment (ML) technology has emerged over the years and its use in automated testing is now useful in many ways. The adoption of AI in microservices testing has allowed organizations to drive better results and achieve greater efficiency. AI also redefines how applications based on microservices are tested.

Use AI in testing software assists engineers and testers by improving accuracy and timing. The automatic test increases the depth of the test performed, allowing for more test coverage.

AI-based software testing is now used to create tests, experiments, and data analytics; therefore, increasing efficiency and improving test accuracy and availability. It also allows easy test retention, especially to manage test data. AI can be useful for successful data modelling and causal analysis. With automatic testing, companies save time and money.

Canary Testing - a small test that quickly and automatically confirms that everything you rely on is ready - is a great help when it comes to testing applications for microservices. AI can improve the automation of canary testing in applications designed for microservices, using concepts such as in-depth learning to identify changes in new code and problems within it or comparisons that occur in a small group of existing users. Everything can be done automatically and there is no need for human intervention.


How to incorporate automation into your microservices test


I will start with a brief reminder of the 5 tips I have given to test microservices:

Manage each service as a software module.

Make important links in the construction of your buildings and want to explore them.

Do not attempt to integrate the entire microservice environment into a small test setup.

Try checking in different settings.

Make good use of Canary testing to get a new code.
Taking each of these pieces of advice in order, let’s see how we can apply it and how to use test automation to help us.


Challenges on how to test microservices


The type of development based on microservices offers many challenges for testing your application. These include:

Availability: Due to the widespread construction of microservices, it is difficult to find a time when all microservices are accessible.

Isolation: Microservices are designed to work in isolation and other freely integrated functions. This suggests that you should be able to examine all things separately and to examine them together.

Information gap: You should have a strong knowledge of each microservice, this will encourage you to write active test cases.

Data: Each microservice can have its own copy of the data. In other words, each may have its own copy of the data, which may be different from the other microservice copy. As a result, data quality poses a challenge.

Transactions: Transactions are always guaranteed at the data level because doing it between different microservices is quite challenging. This is because the agreement can include multiple service calls that are still distributed on different servers.
Typically, a microservices-based system has a variety of services, each of which can be upgraded if needed. There is also the threat of failure and the cost of fixing issues after integration. Therefore, you should have a user testing process for testing applications based on microservices.

                                                     
                                                 

Automation Testing in Microservices

Unit Test: When you develop an application, it may contain a large number of classes, each of which may have several modes. You usually write a test case for a particular unit of code. A unit can be a path, a path group, or an entire category code. Usually, you want to keep the unit tests as private as possible.

The most common way to test a unit is to laugh at external dependence, by successfully testing the business concept. For example, unit tests can be performed outside of a database. This ensures that the tests have no external dependence and are not successful when something outside the test changes.


Layout Test: When we develop business applications, we usually organize them into layers. For example, a web application may have web, business and data layers. You would want these different layers to work well together. Therefore, you write layers for merging tests to ensure their proper integration.

For example, when you call a web layer, is it still distributed evenly in the business layer, and from there to the data layer? Finally, do you get the right answer for the request?


API testing: When we do microservice, we end up giving consumers APIs access to and use resources. Examples are REST and SOAP APIs. You can test the API by writing it to its default test. Even in such an API test, the need for an hour is a source of memory in memory, because it is best to avoid external dependencies.


System Testing: This is where all the programs outside our application go into the picture, during the test. You will start calculating data, external connections and other dependencies required for your application. This is where you move your app to the real world.


User Acceptance Test: This is the final level of automatic testing, where you test all aspects of the end user's usage conditions. Focusing here makes you real-time scenarios, such as access to a production mode database for testing logic. This step is required before making the app live.

Benefits of enabling Microservices automatic AI testing


The benefits of microservices are many and varied. Many of these benefits can be placed on the doorstep of any distributed system. Microservices, however, tends to achieve these benefits on a large scale mainly due to how far they take the concepts behind distributed systems and the focused construction of services.

There are the following benefits of enabling Microservices Automatic Testing -

Promoting better segregation between services and building better programs.

It uses some system design pressure to organize the API in an easy-to-use way.

The test serves as good API documentation presented by the app.

Check each service.

Explore different pieces of application functionality.

Monitoring to assess the impact of change.

Monitor the ongoing operation of your application.


It uses AI for quality assurance


When it comes to digital transformation, many businesses have a vision for customer experience, efficiency, intelligence and profitability that includes modernization, processes and usability. Quality Assurance (QA) is often considered in the background.

However, the entire digital system is always operated on an Agile development framework or in DevOps and translates into shorter output cycles with additional pressure to deliver quality code within set time intervals. To help with this, organizations are setting up other controls on the DevOps side and ignoring the QA strategy. There is a need for change in the quality assurance of organizations. In general, there are two driving forces - speed on the way tests are conducted (continuous quality assurance) and fast marketing time. In order for QA teams to adapt to the agile model of development, traditional automation testing is no longer sufficient, making AI in self-testing inevitable.

Conclusion


Therefore, AI-based test automation for microservices can perform reliable and efficient tests, enabling testers to spend time in test design, maintenance, and analysis. Use AI to make microservice testing equipment will not improve all aspects of software testing but will make testing faster, smarter, and more effective over time. Microservices construction is a distributed method designed to overcome the limitations of traditional monolithic construction. It helps to measure applications with organizations while improving cycle times, however, and it comes with a few challenges that can create additional construction complexity and operational burden.

AI can help you understand patterns and relationships in API calls and come up with more advanced patterns and input API tests. You can use a continuous AI testing process to effectively detect evolved controls.


                                          


About Author

Sanjana Singh

Sanjana is a QA Engineer with skills in Manual Testing and always eager to learn new technologies.

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