If you determine that a REST API aligns well with both your and your customers’ requirements, it’s essential to proceed with designing and validating it to meet those specifications. To gain a comprehensive understanding of how your intended audience will interact with your API, it’s crucial to personally utilize it (also known as ‘dogfooding’) and subsequently, once it’s deployed, continuously monitor its usage. For both phases of API testing, we’ll employ the widely recognized example of a basic contact system or address book.
Begin by establishing a foundational framework with endpoints dedicated to basic contact details such as names, email addresses, and phone numbers. Subsequently, develop your application architecture around these endpoints. If you structure your endpoints to prioritize contact information retrieval initially, subsequent API calls will be necessary for accessing email and address data. For instance, with a contact system comprising ten addresses, you would require a minimum of 30 REST calls to generate a visual representation akin to an address book within your application.
Creating an application solely for API testing might initially appear costly, but it’s unnecessary to develop a fully polished, market-ready app. Instead, focus on building a streamlined version that includes the essential features consumers will utilize to interact with your API. As Nassri pointed out, this approach aids in comprehending the architectural nuances of your API. Furthermore, effective testing involves not only adhering to standards and data flow but also understanding the diverse use cases for which developers leverage your APIs. While many perceive APIs merely as conduits for data exchange, they should be viewed as multifaceted tools offering more than just input and output functions.
Bruno Pedro, co-founder of the Hitch developer community portal, emphasized to SmartBear the significance of REST API in facilitating the creation and automation of functional testing. According to him, testing REST APIs enables the verification of consistent responses resulting from controlled inputs. Pedro further elaborated that achieving successful functional testing entails meeting specific prerequisites:
However, to execute this effectively, it’s crucial to comprehend the intended usage patterns of your API consumers. Nassri highlights that when employing any product or tool for API testing, the tests crafted shouldn’t solely validate input or output; rather, they should consider the potential applications that can be developed utilizing the API—a facet frequently overlooked.
When inquired about the approach for initial API development when user demographics are unclear, he advised formulating a hypothesis regarding the end-user experience and refining it subsequently. He emphasizes that merely identifying endpoints isn’t adequate during API testing; rather, it’s essential to construct an application around them to evaluate usability effectively.
Nassri provides the example of Flickr. While the platform was originally developed as a photo-sharing service tailored for photographers, the Flickr API doesn’t necessarily cater to that specific audience.
Consider your API design as a product: What functionality does it offer, and how does it impact your audience? Assess the user experience and how users engage with it.
Another crucial aspect of REST API testing, besides evaluating response time and data accuracy, is ensuring comprehensive API analytics and reporting. Testing extends beyond mere data exchange; it involves assessing the versatility of your API and, post-launch, adapting and enhancing it based on user requirements.
It’s essential to grasp how end-user developers are utilizing your API. Nassri emphasizes the importance of understanding:
Expanding on the earlier REST API illustration, if it’s observed that users consistently make REST calls for /contacts along with /email and /address endpoints, streamlining your REST API to merge these three into a single API call becomes advantageous. Whether you’re developing your application to test RESTful API scenarios or meticulously monitoring API utilization with a testing tool, identifying such patterns necessitates refining your REST API, thereby minimizing the number of API calls required.
APIs, given their inherent nature as operating over network protocols, facilitate testing across multiple dimensions: behavioral, contractual, and solution-centric. James Higginbotham, API architect and founder of LaunchAny API strategy and design agency, delineates API testing into these fundamental components:
. Behavioral API testing verifies the adherence to expected behavior and adeptly manages unexpected occurrences. It encapsulates the foundational, internal functionality, ensuring the REST API meets anticipated behavior standards and effectively manages unforeseen circumstances. It validates the functionality and performance of the code.
. Contractual API testing guarantees that the implemented code aligns precisely with the specifications outlined in the API definition. Positioned at an intermediate level of necessity, contractual testing validates the consistency between the defined API parameters and the actual code implementation. It assesses whether the API contract maintains its intended functionality as defined, including its response to correct inputs, outputs, and adherence to specified data formats.
. Outcome-driven API testing ensures that the API effectively addresses the intended use cases it was designed to resolve. Positioned as the apex, primarily external value, solution-oriented testing assesses whether the API comprehensively fulfills the anticipated requirements and resolves real-world issues faced by customers. It evaluates whether the API’s functionalities are aligned with the needs and preferences of the user base, emphasizing practical significance and user relevance.
Higginbotham refers to this concept as Maslow’s Pyramid of API needs, comprising ascending priorities from the most fundamental—internal—to the most paramount—external—levels. “Teams must extend their focus beyond merely testing for functional and behavioral adequacy. They must progress upward to ensure that what they are presenting to internal and/or external developers is comprehensive.
Higginbotham proposes an inversion of that pyramid, with behavioral API testing forming the foundation for all API testing endeavors. “I advocate for flipping the pyramid in API testing, allowing a greater emphasis on testing the solution’s automatable aspects, unlike browser and mobile apps. Consequently, this approach directs the QA team’s attention towards validating customer value, while testing at the other two levels concentrates on internal mechanisms to detect bugs in isolation
He highlights that conducting solution-oriented API testing can often pose significant challenges, especially in the case of mobile or browser-based applications. However, he emphasizes that when data is processed through a REST API, this testing becomes substantially more manageable and can yield considerable benefits.
The limitations inherent in REST promote adherence to the HTTP specification, simplifying the test construction process by facilitating the creation of appropriate requests and responses. This contrasts with the intricacies associated with SOAP-based protocols,” added Higginbotham
The testing industry has historically prioritized lower-level tests that validate internal functionalities, often at the expense of concentrating on the actual product deliverables. This emphasis has persisted because a significant portion of UI-focused testing demands manual intervention. However, he argues that this manual testing of user interfaces is indispensable for prioritizing developer experience
The fundamental principles of effective API testing—and software testing in general—are consistent: initiate testing early, perform it frequently and consistently, and leverage automation wherever possible. What renders testing REST APIs notably straightforward is the transparent correlation between data, a relationship further clarified when paired with Hypermedia APIs.
In Hypermedia, adherence to standards facilitates the description of relationships among API entities or data. Nassri elucidated, “By adhering to RESTful architecture, you transmit descriptions from various domains. With hypermedia, you elucidate the connections between developers Hypermedia can emerge as a pivotal component in API testing.
He suggests that while Hypermedia is extensively employed for testing API functionality, its utilization for verifying and validating API behavior remains underutilized. At Mashape, they rely heavily on adhering to the HTTP specification and URLs, contributing to the inherently RESTful design of their APIs.
Bernier reinforces Nassri’s point by emphasizing that due to REST’s high level of compliance with standards, it facilitates the use of description languages such as Swagger, which is utilized by SendGrid. He explains that REST comprises a concise subset of specifications governing the transmission and reception of API calls. It’s the amalgamation with other standards where he identifies a seamless experience for API testing.
You utilize a descriptive language such as Swagger, which aligns with the REST specifications, aimed at detailing REST endpoints and composed in JSON format. Combining these elements enables you to develop code in accordance with the specification and automate processes based on it.
Moreover, as the specification encompasses comprehensive details regarding fields, structure, endpoints, methods, and URLs, it simplifies the process of crafting both rudimentary and sophisticated tests that are amenable to automation.
Bernier further elaborated: “With this descriptive language providing comprehensive information and eliminating all variables, including edge cases, automation becomes straightforward. In software development, automating tasks becomes feasible when edge cases are accounted for. By writing code for each edge case, you can automatically generate unit tests and integration tests for your API. Additionally, automation extends to tasks such as documentation and library generation. It simplifies everything, Bernier remarked.