Successful businesses are always looking out to rapidly explore new markets by promptly introducing new services, and quickly integrating customer demands into existing processes.
For this, every enterprise demands agile, adaptable, and scalable IT technologies that accommodate newer trends and updations to provide outstanding customer service.
Many enterprises all around the globe face several challenges because of traditional, monolithic applications.
These monolithic applications are incapable of quickly adapting to changing business scenarios. This had resulted in several prominent enterprises losing their competitive advantage, especially during the pandemic because they weren’t able to transform business processes in time.
Such enterprises were unable to meet the remote work criteria or were not successful in digitally meeting their online customers’ demands, hence the downfall.
Forward-thinking entrepreneurs and decision-makers, who had already accepted the digital transformation journey were able to expedite solutions delivery, operate at higher speed, and develop solutions that evolve with the business needs.
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Microservices is one such architecture that has been earning recognition across multiple industries and is expected to see an enhanced adoption rate in the years to come.
Microservice architecture has been widely accepted in application and several enterprises have achieved better enterprise agility by completely eliminating the monolithic architecture.
What is a Microservice Architecture
The microservices architecture style development approach is to develop standalone application systems by developing a small set of services.
Each of these individual services or modules operates in its unique process and usually utilizes the lightweight mechanism of the HTTP resource API to interact with each other. These services are built with a focus on completing various business functions and can be seamlessly deployed separately through a completely automated deployment mechanism.
The microservices are also not confined to a particular computer language and can be developed with multiple languages.
Cloud computing provides a model for enterprise applications that can allocate computer resources on demand.
Enterprises can seamlessly deploy microservice-based applications on the three cloud service models IaaS, PaaS, or SaaS.
The cloud models, for instance, PaaS or Platform as a service, provides a cloud platform based on microservice architecture and Docker container technology to help testers with a set of tools or frameworks that enable rapid testing, deployment, development, and maintenance management.
The platforms offer free or paid resources such as infrastructure, middleware, storage services, servers, etc.
The development team will only have to choose the right tool, perform the necessary configuration, and the cloud will build, deploy, and achieve agile development tests and rapid application deployment strategies.
Characteristics Of Microservices
The Microservices architecture has the following characteristics.
- In this architecture, every service is developed using a suitable tool. There are many developer teams working independently
- Microservices will completely eliminate delayed response time as it has an excessive number of service hosts
- There is an individual data storage owned by every microservice
- Modeled around the business domain to offer fast delivery of goals of the business, microservices are aligned vertically with business potential
- Microservices are smaller in size and it works on the policy of the single responsibility principle
- It achieves high cohesion and loose coupling which are the hallmarks of well-designed components
- A microservice is a standalone system. It can work and execute autonomously.
- It allows integrating the microservices using well-known open protocols, interfaces, and standards.
- It offers a heterogeneity of technology, therefore, developer teams can use the stack of technology, which is best suited and comfortable to the applications.
Artificial Intelligence (AI) in Microservice Application Development
The pandemic has led to increased adoption of mobile devices and innovative technologies, expanding adoption of the DevOps methodology, and modifying testing by digital transformation.
Furthermore, manual tests are considered to be much time-consuming and still do not guarantee a 100% accurate result. not full-proof. Therefore, several prominent enterprises prefer to implement automated tests in a CI-CD pipeline, and practice an Automation-first approach.
This is where it gets difficult to test Microservices as it raises several unique challenges to the Testing team.
With microservices growing to become a significant element of enterprise development, applications integrating them will need to be efficiently tested in order to ensure that the services are completely functional and precisely meet business demands.
AI-based test automation of microservices can generate higher accurate tests and can significantly reduce the time required for test creation, maintenance, and analysis.
With Artificial Intelligence (AI), enterprises can also implement deep-learning models, natural language processing, and Machine Learning (ML) to create test scenarios quicker and administer them in the cloud.
Benefits of AI in Microservice Test Automation
Enterprises and QA teams can benefit from AI in creating test frameworks, test execution, and data analysis by utilizing natural language processing (NLP) and advanced modeling techniques.
AI-based Test Automation Services can help increase microservice efficiency by expediting faster releases, developing test precision and coverage. With AI, QA teams can also benefit from easier test maintenance, especially when it comes to managing test data.
Data Modeling Capability
For effective test support, the tester needs the capability to understand how the data is being executed during the test creation. Incomplete data modeling is an example where test maintenance breaks, becoming an obstacle in the complete deployment pipeline. Artificial Intelligence can assist testers with valuable data modeling and error cause analysis.
Repeating each Microservice test manually every time there is a revision in source code can be increasingly time-consuming and expensive. But with the help of AI, QA teams can create automated tests once and you perform them continuously, without the need for an additional cost.
AI in canary testing
Canary testing significantly decreases errors by progressively rolling out the modifications to a group of users before deploying it to a larger audience.
This testing is efficient in the case of microservices testing because the modifications to microservices take place separately from one another, so microservices can be validated individually as well.
While there are a lot of test automation tools to choose from, there is no single-stop technology or solution for microservice automated testing.
QA teams must thoroughly analyze all the convenient possibilities to ensure that the test strategy satisfies all the standards for testing microservice applications. Microservices are surely being adopted in application development and are considered to become the next big thing in digital transformation.
Every enterprise must be capable and willing to adopt the latest technology in order to stay relevant and meet customer demands.
Therefore, if you are implementing a Microservice architecture, make sure you leverage the assistance of skilled testers with knowledge in AI before deploying the final application.