Monday 31 August 2020

Software testing Company in San Francisco

 Software Testing Company in San Francisco

Organizations that adopt artificial intelligence (AI) in the testing of microservices-based applications to get better accuracy, faster results, and greater operational efficiency.

 

AI and machine-learning technology has 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 tested, especially when it comes to testing walnuts.

This increases accuracy; The same steps can be performed accurately every time they are needed. Automated testing can increase both the depth and scope of your tests so that a more thorough test coverage overall. You can also take advantage of the AI ​​to simulate a large number of users interact with your app.

 

Here's how AI-enabled automation can help you test as you scale microservices-based applications, as well as the challenges that you will face and effective strategies that you can do to overcome them.

 

Why traditional testing strategy is not working

Traditionally, when creating a monolithic application, you will be testing each unit of code with unit tests. As the different components of the application that are joined together, you usually test your applications using integration testing of the first and, typically, system testing, regression testing, and user acceptance testing follow.

 

If the code passes all these tests, the release went out.

 Software Testing Company in San Francisco

Testing microservices-based applications is no easy task and not the same as testing monolith; You should be aware of not only the services you are testing but also its dependencies-the services that work with the service being tested.

 

Because the granular nature microservices architecture, the boundaries that were previously hidden in traditional applications are affected. You may have several different teams spread across geographical distances to work simultaneously on different services; This makes coordination extremely challenging. It can be difficult to find a specific window of time to perform end-to-end testing of the overall application.

 

The distributed nature-based microservices development poses many challenges to test your application. These include:

 

Availability: Due to the nature microservices distributed architecture, it is difficult to find a time when all microservices available.

Isolation: Microservices designed to work in isolation along with other loosely coupled services. This means that you should be able to test each component in isolation and testing them together.

gaps in knowledge: You must have a strong knowledge of each MICROSERVICE; This will help you to write effective test cases.

Data: Every MICROSERVICE can have your own copy of the data. In other words, each can have its own copy of the database, which may differ from other copies of this MICROSERVICE. As a result, data integrity poses a challenge.

Transactionality: Unlike the monolith, where transactionality often assured at the database level, implementing transactionality between different microservices challenging, because the transaction can consist of a variety of call services spread across different servers.

Typically, application-based microservices composed of several services, each of which can dynamically increase if necessary. There is also the risk of failure and the cost of fixing a bug or problem after integration. Therefore, you must have an effective test strategy in place to test microservices-based applications.

 Software Testing Company in San Francisco

How to build an effective testing strategy

To build an automated testing process for microservices-based application, you must follow the same best practices you do for other types of testing:

 

 Understand customer expectations as far as test automation is concerned.

set quality objectives and adhere to them.

Analyzing this type of testing is right for you to achieve your goals.

written test in accordance with test pyramid (ie, given that the cost of testing increases as you move up the pyramid).

AI-driven test automation: Embrace innovation

software testers can currently take advantage of the AI ​​for test creation, test execution, and analysis of data by using natural-language processing and advanced modeling techniques. AI-based software testing can help to increase efficiency, facilitate faster release, improve test accuracy and coverage, and allows for easier maintenance test, especially when it comes to managing your test data.

 

For the maintenance of an efficient test, you need to know what happens to your data at the time of the creation of the test. Inadequate data modeling is one of the reasons why maintenance test fails, a bottleneck in your deployment pipeline. AI can assist with efficient data modeling and root cause analysis.

 

Repeating the tests manually whenever changes to the source code can be time consuming and expensive. After you create automated tests, you execute them repeatedly and quickly with no extra charge.

 

Use AI for testing walnuts

Canary testing helps reduce risk by gradually roll out changes to a small group of users before presenting it to a larger audience and application-based microservices it is very useful in testing. In a typical application, microservices changes occur independently of each other, so they need to be verified independently microservices well.

AI can help automate application testing walnut microservices based. You can take advantage of the concept of AI as in learning to identify the new code changes and issues in it. AI can be used to compare the user experience with that small group of existing users, and this can be done automatically; You do not need human intervention in the loop.

 

The challenge in testing the AI ​​based microservices

AI-based testing does have some constraints. Although you can automate functional tests and unit, are very difficult to automate integration testing, due to its complexity.

 Software Testing Company in San Francisco

Some other challenges in the AI-based testing includes the following:

 

Skills

Microservices application testing based on AI-based approaches require extensive technical expertise of testers, and very different from what the manual or automated testers are used to. Testers must be adept at how to use the AI-based tool specifically for applications based microservices.

 

Use cases

One is to use AI to create your unit tests. You can take advantage of the AI ​​to perform static code analysis and determine which parts of the code that are not covered by unit tests.

 

You can also use AI to unit test an update as soon as the changes in the source code, as well as for the creation of test, implementation, data analysis, and testing of the API in an application-based microservices.

 

AI can help you understand the patterns and relationships in an API call and come with more advanced patterns and input for testing the API. You can take advantage of a continuous testing process more efficient AI-powered to detect a control that has been changed.

 Software Testing Company in San Francisco

AI-based testing can not do everything

AI-based test automation of microservices can make the test more reliable, and thus reduce the time it takes for test development, maintenance, and analysis. such tests can in turn be used to check the communication service-to-service, communication line test, etc.

 

You can also take advantage of in-depth learning models and other techniques AI to empower your team to test more quickly build and execute them at scale in the cloud.

 

Adopting AI for test automation microservices no panacea. It will not magically eliminate all the problems associated with software testing. But it can help you build your software testing process smarter, more efficient, and faster-and thus deliver business value consistently.

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