I’m continuing my series on looking at different testing tools, but now that I’ve done a wide range of UI test automation tools, I want to move to different API testing tools. Now, many of the testing tools already spoken about in the UI section can also do a good job at testing APIs, as it is essentially simply passing through a variety of different HTML or JSON commands between different endpoints, and any UI testing tool can do this and assert the received information for automation purposes.
However, there is still a lot of value to be added by tools that are more specifically honed to API automation and provide added benefits that make the role of API testing and automation more effective. And so in the next few blogs of this series, I will be focusing on only those tools that are more specifically aimed towards API automation rather than rehashing the tools already explored. However, if you are already using a multi-purpose tool – especially a licensed one – it’s probably worth your while to continue to use that tool and integrate APIs into your core framework.
Before unpacking different tools though, I thought I would perhaps just do an article on what the core differences are between UI and API testing that tools need to cater for and what makes for a good API testing tool when you evaluate them.
How tools need to handle UI and API testing differently
This is not an article that will detail UI and API testing or their respective approaches to automation (UI Automation and API Automation). I’ve already done that in previous posts, and I would encourage you to read those articles to understand that better. This is a focus primarily on the tools and this section will look at the differences between these different types of tools (or in the case of tools that do both, the different features and approaches that would apply to both.)
Scope: UI testing tools primarily focus on testing the user interface of an application, ensuring that the visual elements, user interactions, and user experience are functioning correctly. They simulate user interactions with the application's UI components and validate the expected behavior. On the other hand, API testing tools concentrate on testing the functionality and behavior of the underlying API endpoints. They interact directly with the API, sending requests and validating responses without involving the UI.
Testing Level: UI testing tools typically operate at the end-to-end or system level, simulating user actions across multiple layers of the application. They often involve navigating through different screens, filling out forms, and verifying UI elements' properties and behaviors. API testing tools, on the other hand, focus on testing individual API endpoints or API integrations in isolation. They perform functional and integration testing at the API level, independent of the UI. While many modern UI testing tools can operate in isolation these days too, like Cypress, Playwright, and Puppeteer, there is still greater complexity in making this work as there is for UIs and so API testing tools don’t need to build in complex feature sets to cater for this.
Test Granularity: UI testing tools tend to work at a higher level of abstraction, dealing with user interactions and visual elements. They verify that the UI components and workflows are functioning correctly from a user’s perspective. API testing tools operate at a lower level, interacting directly with the API endpoints and validating their responses. They typically involve sending requests, validating status codes, checking data formats, and asserting the correctness of API responses.
Maintenance Effort: This perhaps has less to do with tools, but just a keynote to understand our maintenance overall when it comes to API testing. And that is that UI tests tend to require more maintenance that API tests and the tooling will often reflect that with the high level of customization needed on the UI side.
UI tests can be more fragile and susceptible to UI changes, such as layout modifications, CSS changes, or UI restructuring. When the UI changes, UI tests may need to be updated to reflect the new UI structure or to fix broken assertions. API testing tools, on the other hand, are generally less affected by UI changes and require less maintenance effort, as long as the API contracts remain consistent.
Speed and Efficiency: Another point that’s not just tool specific, but architecture-specific, but important to mention anyway. API testing tools are generally faster and more efficient than UI testing tools. Since API tests directly interact with the API endpoints without involving the UI layer, they can execute tests more quickly and with less overhead. UI tests, on the other hand, involve launching the application, interacting with UI elements, waiting for UI responses, and verifying the visual state, which can be slower and more resource-intensive.
How API Testing tools work
Those are some of the basic differences to keep in mind when thinking about API testing tools in general. When you understand some of these differences, it gives you an appreciation of things to evaluate a little differently around API testing tools. However, before we look at evaluation criteria, let’s go through some detail on how they work too.
API test automation tools work by simulating the behavior of a client (such as a web application or mobile app) interacting with an API (Application Programming Interface).
Here is an overview of the typical workflow:
Test Case Creation: Test cases are created to define the desired API interactions and expected outcomes. This includes specifying the API endpoints, request parameters, headers, authentication details, and expected response data.
Test Configuration: The automation tool allows users to configure settings related to test execution, such as specifying test environment details (e.g., URL, port), timeouts, authentication methods, and data sources.
Test Execution: The tool executes the test cases by sending HTTP requests to the API endpoints based on the defined configurations. It includes sending GET, POST, PUT, DELETE, or other relevant HTTP methods as required by the test case. The tool captures the API responses.
Data Validation: The automation tool validates the API responses against the expected results defined in the test cases. It checks for response codes, headers, payload contents, data formats, and other relevant criteria. Assertions are typically used to compare the actual response with the expected response.
Reporting and Logging: The tool generates comprehensive reports summarizing the test results. These reports provide information about the test cases executed, pass/fail status, response data, and any errors or exceptions encountered during the test run. Logging capabilities may also record detailed information for debugging purposes.
Test Maintenance: As APIs evolve, test cases need to be updated to reflect changes in the API structure, behavior, or data. The automation tool allows users to maintain and update the test cases efficiently, ensuring that the tests remain relevant and accurate.
Some API test automation tools offer additional features, such as data parameterization (to dynamically change test data), test data management, test case versioning, scheduling and triggering tests, integration with continuous integration/continuous delivery (CI/CD) pipelines, and integration with other testing or development tools.
Under the hood, these tools typically utilize programming languages and libraries to construct HTTP requests, manage test data, handle response parsing, and perform assertions. They provide an abstraction layer that simplifies the process of creating and executing API tests, allowing testers and developers to focus on the test logic and validation rather than the low-level details of HTTP communication.
Evaluation Criteria for API automation tools
Now that we understand a little better how API test and automation tools typically work, let’s look at the important criteria that need to be evaluated when making a decision on which ones to use. As for what criteria you should look at when evaluating API testing tools, the following criteria are very important to consider:
Ease of Use: The tool should have a user-friendly interface and offer a simple and intuitive way to create and manage test cases. The learning curve should be reasonable so users can quickly start automating their API tests without significant effort.
Flexibility: The tool should support various API types, such as REST, SOAP, GraphQL, or any custom protocols. It should allow users to easily configure headers, parameters, authentication, and other request details to accommodate various API testing scenarios.
Test Case Management: The tool should provide robust capabilities for creating, organizing, and managing test cases. It should allow users to group related test cases, define test suites, and handle dependencies between test cases. Additionally, features like version control, test case reusability, and parameterization can greatly enhance the efficiency of API test automation.
Validation and Assertions: An excellent API test automation tool should offer powerful validation and assertion mechanisms. It should enable users to verify API responses against expected results, including validating response codes, headers, payload contents, and data formats. Support for custom assertions and advanced validation techniques is also valuable.
Test Data Management: Managing test data is crucial for API testing. The tool should provide options for managing test data effectively, such as generating or importing test data, maintaining data integrity across tests, and easily updating or manipulating data during test execution.
Reporting and Integration: The tool should offer comprehensive reporting capabilities, allowing users to generate clear and concise reports on test results and statistics. Integration with popular CI/CD tools, test management systems, and defect tracking systems is also desirable, enabling seamless integration into existing workflows.
Extensibility: A great API test automation tool should be extensible, allowing users to add custom functionalities or integrate with third-party tools and libraries. This extensibility can be through custom plugins, support for popular programming languages, or open APIs.
Scalability: The tool should be able to handle a large number of test cases and execute them efficiently. It should support parallel test execution to reduce test execution time and enable testing against high-volume APIs or under load conditions.
Documentation and Support: The tool should have comprehensive documentation, including clear examples, tutorials, and a helpful user community. Additionally, responsive technical support or an active community forum can greatly assist users in resolving issues and getting timely assistance.
Cost and Licensing: Finally, the tool should have a pricing model and licensing structure that align with the organization's budget and requirements. Free or open-source tools can be suitable for smaller projects, while enterprise-level tools may offer additional features and support.
APIs themselves are easier to often test and therefore the tools themselves tend to be far simpler. However, that simplicity makes it more difficult to often evaluate the difference between tools, so hopefully, these above guidelines along with my breakdowns of different tools in forthcoming articles will help make it easier for you and your testing team, to pick an API testing tool that will be most effective for success.