Nowadays, artificial intelligence (AI) has significantly contributed to software testing with solutions across every major domain.
It has helped testers take advantage of various use cases ranging from test creation and test execution to data analysis and natural language processing (NLP) These techniques have immensely enhanced testing and have helped testers increase efficiency, promote faster releases, provide better test accuracy, and coverage.
Test automation of microservices is another domain where artificial intelligence has created enormous potential for enterprises as well as QA analysts. It has helped them develop more reliable tests with faster time to market, thus delivering microservice-based applications quicker.
What is a Microservice Architecture?
Microservices is an innovatively unique architecture used in building extensively large applications. Since the large applications have different modules all working together, Microservices help in dividing it into a set of modular components or services with separate technology or language.
Each module supports a speciﬁc task or business objectives and uses a well-deﬁned and straightforward interface, such as an Application Programming Interface (API), to communicate with other sets of services.
In recent years, microservices have become more popular, and companies prefer to move from a monolithic architecture to Microservices Architecture (MSA).
Normally, there will be up to thousands of microservices included in large-scale MSA-based systems with all the microservices interconnected with each other.
For example, there are more than five hundred microservices implemented on Netﬂix that manage almost 2 billion API edge requests every day. Similarly, Tencent’s WeChat application uses 3,000+ microservices that run across 20,000 machines.
Benefits of Artificial Intelligence (AI) in Microservice Test Automation
The use of artificial intelligence (AI) in automated testing is the latest trend in the efficiency assurance of microservice architecture. It helps in running more automated tests across multiple microservices, simultaneously.
Since each microservices are based on different languages, the AI can generate unique test scripts based on every language. This makes tests faster and can significantly reduce costs because the number of resources required to build test cases for each microservices can be reduced with the help of AI automated testing.
Since microservices comprise scalable architectures to include more services or modules as the application expands, it also helps in including more test cases whenever necessary. It can help the QA teams to add multiple architecture components, quality attributes, etc to test MSA-based applications.
More precisely, with the implementation of Microservices and Artificial intelligence (AI), test automation can integrate MSA design components such as services, distributed architecture testing strategies, and microservices interaction methods.
The interaction methods can be both synchronous as well as asynchronous protocols and can be used to create test cases for testing MSA-based applications. Moreover, there are multiple use cases that are utilized to test and evaluate the reliability, stability, and architecture of MSA-based applications by implementing a scalable architecture.
DevOps and Continuous Integration (CI):
DevOps has a range of practices that range from Continuous Integration (CI) and Continuous Delivery (CD) to multiple use case testing and deployment. All these practices are together intended to deliver reliable software systems by encouraging close cooperation between development and operational staff.
Microservice Test automation with AI is a key factor in succeeding with DevOps, as automated methods significantly enhance the regression testing of microservices and help in continuous delivery.
There have been numerous microservice-based AI testing architectures that are created and tested for MSA-based applications on Open Network Operating System (ONOS). They are established in the CI environment and are also widely used as a trial topology method to efficiently evaluate ONOS applications.
The microservice-based test automation has also enhanced the quantitative behavioral aspects of testing. These tests have a deep focus on the performance of an application, mostly across the production phase of MSA-based application development.
For instance, leveraging a different approach to evaluate individual microservice performance can be done by integrating test speciﬁcations. Moreover, with AI technology, testers will be able to test and compare the performance variations between monolithic and microservices applications in the ﬁeld of Network Function Virtualization (NFV) by using the analytic model and implementing testbed experiments.
The Different Types of Testing That Can be Conducted Using AI
This Granularity testing in a microservice architecture is entirely focused on testing the speciﬁc functionalities of software systems. This type of testing is challenging because of managing chaining interfaces and increased asynchronicity complexities of MSA-based applications.
This AI-based testing focuses on evaluating application response and performance, mainly the response time, resource consumption, and scalability. Performance testing is challenging for Microservice-based applications because there are hundreds of inner applications present due to which the introduction of AI will provide more potential.
It will be able to test new business requirements in the operational phase and identify poorly organized microservice architectures, thus enhancing dependency between hundreds of microservices.
This testing focuses on finding inaccuracies, defects, and crashes on software/applications at runtime. Creating and completing tests at runtime is challenging because of the large ﬂexibility and evolvability nature of microservice architecture.
However, runtime testing is essential for dependability evaluation of microservice architecture-based applications in their operational phase.
Microservice-based test automation services are composed of powerful veriﬁcations that analyze a complete system to decide whether the expected behavior of a ﬁnite set of test cases is met.
The tests are structured in a series of activities that have a purpose of ﬁnding the possibility of implementation, quality, or usability failures of a program or system.
The number of services, interconnecting processes, instances, network communication, and other variables inﬂuence testing methodologies in microservice applications. Microservice architecture and applications face considerable challenges in testing because of their complicated nature and dynamic behavior.
During the testing process, it needs to understand the concurrent behaviors of the various microservices and interactions between them. In the upcoming future, there will be a huge industry potential for managing the testing complexity in Microservice-based applications.
Therefore, it is essential that every test automation service provider update their technological domain and integrate emerging trends such as AI and ML to stay competitive. Also, read about the Benefits of Implementing Conversational AI and many more articles on technosdaily.com