What is an API?
Application programming interfaces, or APIs, provide a way by which software systems can communicate with one another. More technically, an API is a mechanism that programs can utilize to access data or leverage functionality managed by another system.
APIs are services that are consumed by developers. An API is made up of a set of procedures that allow development teams to access data and functionality from other systems without exposing them to the complexity of the underlying implementations.
APIs allow developers to quickly interface with the systems responsible for handling data as they construct features for their end users. Furthermore, when developers utilize APIs, they just need to understand how to effectively build the API calls they need to make and how to comprehend the results. This keeps things simple for the consuming developers, as it does not demand that they have an in-depth understanding of the logic that resides behind the scenes.
What is API Testing?
Like every software product, APIs need to be evaluated. This testing is done to check that the procedures that make up the API function perform as intended (among other things). The process of validating each facet of an API procedure is termed API testing.
As indicated previously, APIs are created for use by developers, not the usual end user. While an end user interacts with an application through a user interface, a developer interacts with an API programmatically, writing code to generate API queries and then assessing the results. Therefore, API testing will differ substantially from the type of testing that is done to evaluate the appearance and feel of an application.
When Testing an API, What Should We Test?
To better show what this looks like in practice, let’s take a deeper look at various types of tests that need to be conducted to completely validate an API.
Testing for proper function
When testing an API, it’s vital to test each endpoint for appropriate function and to investigate all situations in an effort to give thorough test coverage. This includes testing all versions of each request and checking the response for each variation.
For example, let’s imagine the situation of a REST API endpoint for performing a GET request that is responsible for retrieving a group of items from a database and sending them in JSON format to the client application. The client may be provided with the ability to pass various arguments to the endpoint to receive different result sets. For instance, the call might be structured to accept a product category, minimum or maximum pricing limitations, etc. This procedure would need to have a test case built for each potential combination of arguments in order to validate that the proper data is returned in each circumstance. This
includes evaluating requests when only the product category is supplied as a parameter, where the product category and the minimum price are passed as parameters, and so on.
Furthermore, it’s crucial to verify that the data being returned is formatted as intended, thereby guaranteeing that it can be read and utilized programmatically with ease. Finally, test cases should be written to check that the appropriate HTTP status code and error message are returned in scenarios when the request fails.
The following are a few of the problems that functional testing of an API can uncover, though they are not all of them:
- requests that don’t receive the required response (for example, a GET request with parameters that don’t properly filter the results).
- requests that yield badly structured answers, making it difficult for client programs to use the data they supply efficiently.
- exceptions that are treated surprisingly.
Performance testing (load testing) of APIs
Performance testing, often known as load testing, is another essential component of API testing. Even while performance testing an API yields more information, some businesses would rather restrict the notion of load testing to a certain kind of stress testing, possibly in different traffic patterns. More and more, SREs and other owners of applications and performance choose to delegate API performance testing to monitors (continuous API tests) that may carry out load and functional testing simultaneously in order to validate performance holistically. This allows engineers to immediately identify when an API fails under pressure, as well as why it failed and how to resolve the problem.
To make sure the API won’t become sluggish when it’s put into production and used in client applications, load or performance testing is carried out. Teams can assess how different load and traffic patterns affect the performance of an API by using load or performance testing.
Since non-production environments rarely have the conditions that give rise to these kinds of issues, diagnosing performance issues can be challenging. In a development environment, for example, manually hitting an API endpoint to assess call performance is not a true test of performance since it is not subjected to the volume of concurrent requests that it is likely to see in production. Furthermore, compared to a production context, an API’s working datasets may be substantially smaller in a development environment.
Reusable functional and performance API tests of today can be implemented in any kind of environment. Since they can be scheduled independently of CI/CD platforms, there is minimal chance that the functional API monitor will produce inconsistent results when endpoints are changed in production settings.
Because of this, it’s critical to test API queries against dynamic datasets that accurately reflect the data that will be present in production to make sure that, when more data is analyzed, complicated processes don’t become unpredictably costly. In order to assess API performance in various contexts, it’s also critical to replicate a range of traffic patterns.
Overall, the owners of the API may reduce the likelihood that they will be the cause of performance bottlenecks in applications that use their services by doing performance testing.
The following examples, though not exhaustive, should be verified to make sure an API functions flawlessly in production.
Testing the design of API procedures
This can reveal issues with performance as datasets grow (e.g., an API GET request made in a local development environment performs well due to the small dataset it is running against, but experiences slowness against larger, production-like datasets due to poor query design).
Testing API performance against various traffic patterns
- Request volume consistent with that expected in a production environment.
- Sudden spikes in request volume are experienced for a short period of time.
- Abnormally high request volume. This testing is conducted to determine the type of traffic the API can handle in its current state without experiencing a significant drop-off in performance.
Automated API Testing
As is true with any software product, testing an API is accomplished most effectively when automated. With automated API testing, a variety of benefits are realized.
Human error is an unavoidable aspect of manual and repetitive activity. It’s reasonable to expect that a human will be able to develop a suite of automated tests to evaluate API functionality, but it’s not as reasonable to expect that he or she will be able to manually execute each test case and evaluate the results without missing something somewhere along the line. Furthermore, manual testing takes significantly more time to perform than automated testing. Due to that fact, manual API testing cannot be conducted as often or as thoroughly as automated API testing.
One of the most effective ways to leverage automated API testing is as part of a CI/CD pipeline. This means conducting API testing continuously, as an API is developed and code changes are integrated. In doing so, modifications that result in unexpected consequences will be detected immediately.
Take functional API testing, for instance. Imagine a developer modifying functionality that backs an API procedure for pulling a list of products from a database. They could make a change that unintentionally breaks filtering when results are retrieved under specific circumstances. While this developer may not catch this error prior to delivering their changes, automated functional testing of this API — configured to run as part of a CI/CD pipeline — will ensure that this problem is flagged the next time it’s kicked off. By doing so, this testing assists in preventing breaking changes from infiltrating production releases, thereby ensuring a consistent and positive experience for consuming applications.
Conclusion
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