Interdependence and collaboration between businesses and commercial sectors have changed in recent years with the use of Application Programming Interfaces (APIs). APIs have been around long enough for companies to know that they can use open web technologies to extend their services via in-app integrations. APIs allow two programs to communicate with one another and exchange data in a smooth manner. Once an API has been created, it is vital to test the interface to ensure that it is functional.
API testing validates the functionality of API endpoints to measure their performance and response based on security, functional correctness, or just a status check. During API testing, testers use software to send API calls to the endpoints and take note of the system’s response while also paying attention to the business logic. In the Software Development Life Cycle (SDLC), testing is done either on the user interface (UI) or the API.
While UI testing focuses on assessing graphical user interfaces, such as the ways in which users interact with applications and elements like layouts, icons, toolbars, buttons, windows, and colors– API testing focuses on performance constraints that affect business logic in addition to the ways in which data moves between different communication protocols and programs. The main challenge faced with UI testing is that it’s slow, flaky, and inadequate. The UI tests are dependent on the stability of the UI, and changes made to elements of the UI might lead to false failures, which must be taken into account. Additionally, UI tests are slower to execute than API tests.
Since more and more businesses are adopting the agile methodology, test automation has become an essential part of Continuous Integration and/or Continuous Deployment (CI/CD) pipelines and microservices frameworks. This is due to the increasing need for faster development and delivery, which reduces time-to-market (TTM). In DevOps, the CI/CD pipeline is an iterative and incremental model of software development wherein different stages are automated and phased into sprints in which functionality is developed, tested, and deployed rapidly.
To encapsulate business scenarios, most software development strategies use microservices architectures, wherein a single application is composed of small services that each run independently. Since microservices break things down into components that are easier to manage, design, and deploy independently, there are more APIs to test. As a result, automated REST API testing is an essential phase of DevOps, including CI/CD and microservices.
The increasing number of APIs being established and integrated has increased the need for automated REST API testing. Postman, Rest Assured, JUnit, Karate, SoapUI, ReadyAPI, and Sauce Labs are just a few of the frameworks and tools that have been developed to make API testing easier. Continue reading to learn more about the relevance of automated REST API testing in today's DevOps, including microservices and CI/CD pipelines.
Traditionally, end-to-end API testing has been left to developers while the rest of the QA teams focus on UI testing. As more and more companies adopt API-centric products, however, there is a need to diversify testing methodologies, including automated API testing. This is because integrating automated API testing within the CI/CD pipeline will not only streamline the development process, but it will also allow the QA and operations teams to collaborate in DevTest processes. Other advantages of integrating automated API testing into CI/CD pipelines include:
Given the complexity of APIs in modern applications, test scenarios must cover a wide variety of factors, including technologies, endpoints, API data, requests, answers, and business logic levels. Manual testing makes this challenging, especially when huge datasets and multiple use cases are involved. API testing systems can automate around 95% of the important testing definition, execution, and maintenance operations, allowing for massive increases in test volume and scale.
Teams gain efficiency and productivity when automated API testing is integrated into the CI/CD process. This is because automated API testing increases the number of test cycles and the variety of tests that QA teams can accomplish in a given amount of time. As a result, the time spent on testing is minimized, thus reducing time-to-market.
As cloud computing becomes more popular, testing increasingly necessitates observability and monitoring, particularly when it comes to CI/CD pipelines. Observability is achieved throughout CI/CD pipelines using automated API testing, which provides insights into API functionality, performance, security, and test automation.
Software testing has always been inefficient and expensive, particularly when utilizing in-house open-source frameworks or traditional API testing tools. This is because each test requires space and time, both of which expand linearly as the project's scope grows. Because more sprints necessitate more time and space, the processes must be automated and conducted simultaneously. Testing coverage is substantially wider and faster using automated testing tools and frameworks, which lowers the total cost of software testing. When properly implemented, automated API tests lower costs over time, allowing businesses to receive more value for their money.
End-to-end testing is becoming more popular in the API industry, both among organizations that sell APIs as products and those that have integrated API-based services into their applications and need to assess how well they work. As previously noted, automation is a key step in CI/CD pipelines and microservices, and it should not be overlooked while testing APIs. Automated API tests are not only faster to execute, but also ensure continuous API testing, resulting in more efficient maintenance and debugging.