9 Types of API Testing to Ensure Performance and Security
APIs lie at the heart of modern software development. They transfer data to link systems, applications, microservices, and other components. With the rise of API-based products, over 90% of developers use them, and 30% of their time is spent building APIs, according to Nordic APIs. Before pushing your API to production, you must thoroughly test it to uncover hidden issues. You need to be sure that the servers can handle heavy loads and that the API will not break at runtime. Different types of API testing are necessary to ensure that your API performs well, regardless of expected or unexpected requests.
Testing is about examining your API to ensure that it meets expectation thresholds, from functionality to performance and security to overall reliability. By running API tests, development teams can ratify the quality of APIs and assert applications' core functionality even without a ready user interface. Instead of catching errors and weaknesses later in the development process (which can be costly to fix if you have to update large chunks of code), tests reveal these breakpoints early enough through automation; otherwise, they would require manual execution.
So, what are the different types of API testing? In this article, we round up 9 types of API testing, give a description of each, and link to a few resources. We will also recommend some API testing tools based on each strategy. These tests are not arranged in any particular order, it all depends on your workflow. There are no right or wrong ways to test APIs.
1. Smoke Testing
Smoke testing in an API ensures that important features work accurately after a build or an upgrade. A typical API smoke test involves the following components:
- Endpoint verification: to validate accessibility and expected HTTP status code return.
- Data validation: to show that the API returns the expected data structure for sample requests, authentication, and authorization.
- Authorization: to assert that the API handles all user permission scenarios.
- Error handling: to confirm that the API provides meaningful error messages based on conditions like invalid inputs or missing parameters. It also checks for performance and response times to measure the time taken for sample requests.
- Consistency checks: to validate API behavior across different environments, from development, through staging to production.
- Edge case handling: to check the API’s ability to handle boundary conditions such as empty inputs or extremely large parameter values.
The best time to enact this common API testing type is post-development, with every deployment and for every deployment environment. The process can be simplified in a few steps if you are in a continuous deployment pipeline.
First, decide what to test, as this isn’t the place to test all your API can do. An example would be testing paths for each endpoint, significant variations, and security levels if authorization is needed. Then, based on your tool choice, export your tests into JSON files and write a script to run these tests. Create a deployment step to call your script, and lastly, organize your variables for different environments. You can group them into variables that change for each test environment, those that don’t change, and those that change and need to be kept secure.
For this type of API testing, you have several API performance testing tools, including Postman, Newman, PowerShell, and Octopus, to set up and automate workflows. Other tools include Selenium and Appium (for web and mobile APIs), Jenkins, Katalon Studio, REST-assured, Assertible, Soap UI, Mizu, and the Endpoint-smoke test tool. Some resources worth checking out to get started with API smoke tests are running API smoke tests in a continuous deployment pipeline, automated smoke tests for REST APIs, and API smoke tests with Postman monitoring.
2. Functional Testing
Functional testing plays a crucial role in the quality assurance of APIs by verifying that all features match the required specifications and perform as intended. In this case, the processes involve specific data entry, output analysis, and comparing the expected result and actual values. API functional tests involve the following features: endpoint URLs, request payload, response data, error handling, and performance. The key idea is validating API functions against requirements.
API functional testing is procedural. It begins with a planning and design stage, defining clear test goals per the API documentation. A good example is a payment gateway API with sample scenarios like successful payment handling or response to expired cards. Next, create test cases and accompany each with a step-by-step guide on setting up the test environment, running tests, and validating the response.
When running tests, you can choose between doing it manually or automating with functional API testing frameworks. The last step is analyzing the results by counterchecking response data, evaluating timing, and assessing error codes.
Notable challenges in running functional API testing are access to changing security tokens for testing, adequate API documentation, ability to test dependencies fully, full capability to test API sequence calls, and tracking all API’s ongoing changes.
In terms of API testing best practices, the best time to implement functional tests is to reveal weaknesses early in the development process before releasing them to the public and, in some specific cases, during integration or regression tests. To effectively test, check out Katalon’s list of 10 automated functional testing tools, Testfully’s ultimate guide on functional API tests, and Testsigma’s explanation of the importance of functional API tests.
3. Integration Testing
API integration entails linking various software systems to work as a unit through APIs. In the context of APIs, integration testing ensures that every integration component works well with the API. Authentication, rate limiting, pagination, and API response bodies are all components that need to be checked in this case.
Performing API integration tests aims to ensure interoperability, validate business logic, enhance reliability, and promote scalability. Differently put, it is testing combined API calls for end-to-end scenarios. API integration tests are often done after unit tests, during continuous integration processes, and before major releases.
A few steps are involved in performing API integration testing. Test the environment by identifying the APIs, creating a test environment, installing required tools, and configuring the environment. Next, create a testing strategy. A simple one could involve defining the scope of tests, identifying all scenarios, sequencing API calls, and defining success criteria. Proceed further and test as you document all test cases. There is also a need to update the tests with API rollouts.
Some API integration tools you can try include Postman, Swagger (OpenAPI), SoapUI, JMeter, Curl, and REST-Assured. For practical and in-depth guides to API integration testing, please look at How to Do API integration testing right by Luxe Quality, Merge’s guide on running API integration tests, Postman’s integration testing template, and API integration testing made easy on Dev.to.
4. Regression Testing
In software development, regression tests ensure that functions do not break after updates or feature additions. In APIs, regression testing evaluates an API-based application’s functionality on changes, enhancements, and updates. Regression tests provide consistent stability by improving the overall quality and strength of APIs, customer growth, and overall cost-effectiveness. When creating tests each time is a norm, teams can bypass large, expensive bugs. It also helps to comply with strict security standards through regression for healthcare, government, and financial industries.
Regression testing is applicable on several occasions, including updates to an existing feature, adding new functionality, notable codebase fixes, optimizing source code, adding patch files, releasing a new software version, config changes, user interface (UI) changes, or integrating a third-party into a system. In the mentioned conditions, all instances involved restructuring the current code, which may lead to unexpected behavior.
Regression testing ensures updates don’t break existing functionality. Rather than automating validations and request-response from the API, you have a modular and reusable test suite. The test suite should cover data values and all involved business processes.
There are plenty of tools you can use for this API testing type. These include ReadyAPI, Postman, REST-Assured, Katalon Studio, SoapUI, API Fortress, and WebInject. Some other notable mentions are Apigee, JMeter, and Tricentis Tosca. To begin exploring regression testing for APIs, please check out differential regression testing for REST APIs, Katalon’s guide on software regression tests, and this brief introduction to regression testing with Postman.
5. Load Testing
Load testing evaluates how APIs perform under different loads by simulating instances of user activity. Only after completion of a specific unit or codebase is load testing conducted to check if the theoretical solutions work in practice under given loads. Three scenarios emerge for load testing.
First, the testing is done under regular traffic, hence the name baseline. Second, the tests are based on the maximum theoretical traffic, ensuring that methods handle requests correctly on full loads. Finally, an overload test comes in place, with a 10% or 20% increase in traffic. Besides asserting that the API can handle the expected load, this can be a good way to optimize, improve performance, and pinpoint latency or bottlenecks.
The process typically begins with small, isolated components and gradually increases load and test complexity. Tests are scripted with user flows and defined parameters to ensure the responses from the API meet service level objectives (SLOs). Simulating realistic traffic patterns helps uncover system limits and assert reliability in more complex scenarios like testing integrated APIs and their end-to-end flows.
To drive meaningful tests, determine the object. For instance, to validate reliability under expected traffic or breakpoint discovery under unusual loads. Different load test options like spike and stress tests can be used by simulating virtual users, working on a specific request rate, or validating SLOs using thresholds. The key is to start with simple tests and iterate progressively to expand the test suite as needed.
Top tools for API and load testing include JMeter, Loadmill, Artillery, Gatling, Blazemeter, Telerik Test Studio, and Postman. For practical guides into API load testing, Postman has a guide to API performance testing, and Grafana Labs has a beginner’s guide to API load testing. Here’s another guide on using Postman for load testing.
6. Stress Testing
Stress testing involves putting the API under loads above its operating capability to evaluate how it scales. The aim is to determine the API’s breakpoint and identify the maximum capacity of the API, vulnerabilities, and potential bottlenecks. By testing under extreme conditions, developers can identify areas where the API performance degrades, establish a maximum threshold for which API becomes unstable, and optimize the API’s infrastructure, configs, or code to reinforce its resilience and performance.
High-traffic simulations are easy to do with all the load testing tools mentioned in load testing. Consider a case where you use a tool like Postman. All you have to do is sign up for a Postman account, create a new HTTP request, enter request methods and parameters, send the request, create a Postman collection, and add requests to the collection. You can use the collection runner to execute many requests at once. Afterward, you can analyze the results.
Some great resources to dip into API stress testing include stress testing by Grafana Labs, how to load test and tune the performance of your API, a practical guide to using JMeter for performance and load testing, and this overview of stress testing in APIs.
7. Security Testing
Leveraging an API security testing checklist confirms that implemented security measures protect the API against potential vulnerabilities, threats, and attacks. The process involves detailed tests covering authentication techniques, authorization, input validation, data integrity, encryption protocols, and error handling, among other security concerns. Ideally, API security testing is best done after functional testing—once the API works as intended, move forward to security—although there can be an overlap between the two.
Modern software development acceleration in development and release times through continuous integration/continuous deployment (CI/CD) makes it easy to ship security vulnerabilities to production.
There are four main benefits of addressing API security concerns: protecting sensitive data, ensuring continuous service availability, compliance with legal standards, and improving the trust of API users and stakeholders. An API security testing workflow entails planning, defining scope, vulnerability assessment, exploitation, reporting, and remediation. It's easier said than done.
There are several types to consider when working on API security testing. Static analysis security testing (SAST) finds vulnerabilities in the source code, dynamic application security testing (DAST) reveals problems likely to occur at the time of use, and software composition analysis is there to examine the design, code patterns, and how much code is written for each task. Authentication and authorization are also essential to verify identities and provide access.
For hands-on practice with API security testing, please check out Akamai’s guide to security testing, Synk’s guide to API security, and Stackhawk’s comprehensive guide to API security tests. In your API performing testing tool pack, consider having Astra Pentest, ZAP, Postman, and Synk.
8. UI Testing
API UI testing refers to examining the user interface of an application that interacts with an API to ensure that the UI components that rely on API calls work accurately, respond appropriately to various inputs, and display the correct data. Tests in this scope are done after API development, before release or deployments, when integrating with third-party APIs, after UI changes or refactoring, during CI/CD pipelines, on critical user journeys, in the events that you suspect of API incompatibility, and during software regression testing.
In a typical API UI testing workflow, all you have to do is identify UI elements interacting with the API and configure your test environment to include the API endpoints, authentication, or any necessary data. Then, simulate API requests, validate responses, and check the UI behavior. Your test script needs to assert that the UI works as expected. This could include the presence or absence of UI elements, data accuracy, and UI’s response to different status codes, e.g., 200 (OK), 400 (Not Found), and 500 (internal errors). You can also opt to automate the tests to run regularly.
For practical immersion in API UI testing, look into this GitHub repo on API Testing and development with Postman, the Selenium Documentation (which can be extended to test APIs via UI), the Cypress Developer docs on API interaction with UI tests, and Postman’s learning center, a great resource for building your API testing strategy.
9. Fuzz Testing
API fuzz testing is all about automated tests with random, invalid, unexpected, or distorted data fed to the API to reveal bugs or identify crashes. Even without the source code, hackers employ fuzz testing to identify software flaws through DDoS attacks and SQL injection.
Although fuzz testing identifies vulnerabilities by working with unexpected inputs, it does not provide a clear picture of the security posture and is not a replacement for security testing. Techniques like penetration testing and code analysis can be employed to enhance security. Fuzz testing, however, is done before production in CI/CD pipelines.
Fuzz testing can be one of three categories. First, fuzz tests can be based on the level of input awareness. If the data generated is purely random, it’s dumb fuzzing; if the random data matches API input requirements, it’s smart fuzzing. The second category of fuzz testing is based on how inputs are generated. In this case, changes to previously accepted inputs are called mutational and generational, where new random data is used based on the analyzed valid input format and structure. In the third category, the awareness level is in the program structure. In this case, it is either a black box if the tester does not know the codebase or a grey box when partial or full source code is available.
There are plenty of tools for API fuzz testing. They include RESTler, Wfuzz, Boofuzz, Fuzzapi, and jdam. You can find some fuzz testing repos on GitHub, including APIFuzzer by KissPeter, Microsoft’s restler-fuzzer, and rest-api-fuzz-testing still by Microsoft. For practical, interactive guides on API fuzz testing, check out this step-by-step build and fuzzing guide for Python, Rapid API Guides’ overview on API Fuzz testing, and this tutorial on fuzzing APIs, which shows you how to catch vulnerabilities using fuzzing.
Conclusion
In this article, we have explored 9 types of API testing and when to run them. While we have given you an overview of all the API test types, diving deep into each is up to you. It might be impractical to incorporate all these tests in your workflow, and therefore, you may have to make some trade offs based on your current business needs and available budget.
Choose wisely! When approached well, these different types of API testing are a good way to ensure API reliability and security.
Again, there are many other dedicated API testing tools. The ones mentioned here are good choices due to their transparent and fair pricing (even in commercial setups), good free tiers, and good developer experience, as most can be easily integrated into a developer’s toolset.
If you want to learn how to build APIs and not just test them, learn how to build and document a GO REST API with Gin and Go-Swagger.