Today’s software testing trends show the growing demand for more efficient and automation-oriented API testing. Many of the current test automation solutions focus on the UI, while most API-level testing is still done manually. As more companies focus on creating highly efficient and agile environments, testers are in need of easy-to-use, intelligent automation tools for testing APIs.
Conversely, inefficient API testing can result in businesses not discovering issues with their cloud services in time — meaning customers are likely to be impacted by them. Plus, manually writing tests is time-intensive for developers, further increasing the need for tools that can help developers test their cloud services using real-world scenarios.
With this in mind, we’ll take a look at the Top 5 API testing tools available on the market today.
API Testing Essentials
Creating a good user experience is at the core of making a good product, and proper testing can play a key role in ensuring that no user experiences issues with your product. With a good testing tool you can verify reliability, performance, and security as part of the software development process rather than waiting for user reports.
This section covers in general terms what proper testing looks like, showcasing key aspects of testing that every developer should be familiar with and consider.
Determine a Strategy
Given how many different types of tests can be run—unit tests, integration tests, load tests, etc.—it’s crucial to have a proper strategy before moving forward. Having a strategy makes the entire process much more efficient and insightful.
For example, in most cases you want to start with unit testing, ensuring the functionality of your application. Then you move on to more advanced tests such as performance testing. Having a testing strategy allows you to plan tests according to how quickly they run and how likely they are to catch common issues, on top of streamlining the development process as a whole.
Continuing the idea of creating an efficient testing process, you need to consider how automation can become an integral part of your workflow, which becomes much easier once you’ve defined a streamlined testing strategy.
Many companies are performing some kind of test automation already, from creating scripts that execute a series of sequential commands that would otherwise have to be run manually, to automating most of the execution and creation. The benefits of doing this—consistent execution, catching bugs early, increased test coverage, repeatability, etc.—goes for automating other parts of your testing as well.
In the software development world—especially in modern cloud and microservice environments—it’s easy to assume that test automation inherently means integration into CI/CD pipelines. However, test automation can be as simple as writing a script to ensure consistency between test runs. That said, there are major benefits from implementing testing continuously, as you’ll see in a few sections.
Use Real-World Data
As you automate more and more parts of your application, you’ll need to ensure that the tests provide as much insight as possible, which is only possible by using real-world data. This is especially crucial once you start integrating tests more deeply into your infrastructure, like what’s covered in the next section.
Considering how varied user behavior and data is becoming in the modern world, using real-world data is important. For many companies, it’s no longer enough to generate a few rows in a database for testing—it simply won’t simulate real-world conditions accurately enough when accounting for the complexity, scale, and variability of data experienced in modern production environments.
Consider Continuous Implementations
Adopting automated API testing is a crucial part of improving developer efficiency—as covered previously—subsequently delivering high-quality software and reducing time-to-market. But implementing this in a continuous manner takes these benefits one step further.
The automation of testing makes it much easier for developers to run more reliable tests, but you’re still relying on those tests being executed manually. The true power of automated testing comes from being able to create gated checks as part of your pipeline, ensuring that all code meets certain criteria before it can be deployed to production.
This is why you should consider continuous implementations of testing, ensuring that even the more advanced testing methodologies, such as performance testing, are being performed in some capacity as part of new pull requests.
Configure a Testing Environment
Setting up a dedicated test environment is absolutely critical, especially once you start incorporating continuous testing. Separating the surrounding environment of different tests allows for isolation and evaluating your application under controlled conditions, without possibility of interference from ongoing development or other developers’ testing.
A well-configured test environment should simulate production as closely as possible, which includes software, hardware, network configuration, and, perhaps most importantly, data. It’s already been mentioned in a previous section, but the importance of realistic data in testing simply cannot be overstated.
Collect and Analyze Data
So far, any mention of data has referred to the underlying data used by the application during testing. However, the tests themselves are, of course, going to generate a variety of data as well—data determining the success of your tests.
During test executions, you should gather information about typical metrics such as CPU usage and RAM usage, infrastructure-specific metrics such as number of Pods when testing autoscaling in Kubernetes, errors and anomalies that happen during testing, performance metrics such as transactions-per-second, etc.
The point is, simply running a test and verifying that the application can run is rarely enough. In tightly-coupled microservices where latency can build up, collecting and analyzing performance metrics is crucial. If your application can successfully handle all requests you generate, but all with a response time of 2 seconds, is that still a success for you?
Implement Automatic Mocks
Most of the principles mentioned above can only be truly efficient and realistic by implementing automated mocks. How can you create a separate environment for each test, seeding realistic data, and have that be done on every pull request? There are many ways of doing it using common tooling:
- Use Docker containers to create reproducible dependencies and seeding data by importing it from external files, or generating it with scripts during the build process
- Deploy new Kubernetes clusters with lightweight tools such as K3s
- Create new
gitbranches for each test, including the data in your new branch
- Use tools like Terraform or Azure ARM templates to dynamically spin up new infrastructure and tear it down
The above are just a few examples of the many ways to create test environments. But they all lack one key identifier: efficiency. For every option that has to seed data, how do you keep it up to date? For every option deploying a new instance of dependencies, how do you keep them up-to-date? It’s possible, but requires a lot of engineering hours.
The most optimal way of creating these isolated environments—also known as preview environments—is to use mocks that are automatically created. Mock servers have the benefit of being almost entirely static, with no other logic than perhaps a matching algorithm determining the most appropriate response to a request. This allows mock servers to be run using few resources, while being much less complex than creating development instances of actual applications.
But, it’s important that these mock servers are configured and created automatically, as there’s still the concern of seeding data. One approach is to continuously record traffic from your production environment—both incoming and outgoing—then using the recorded outgoing traffic (and the responses) to generate your mock servers. This approach combined with the use of recorded incoming traffic is known as production traffic replication.
5 API Testing Tools
All the essentials outlined above are based upon years of experience working with testing in modern cloud environments, and will hold true for most engineers. That said, as with everything in software development, it’s important to consider what your situation is and what you need, as it’s rare for anything to hold true for all engineers.
Below you’ll be presented with 5 different options for API testing, each with its own benefits and most optimal use case. When reading through the descriptions, keep in mind what’s most essential for you.
Postman is among the most popular tools for API testing—and with good reason. Its collaboration platform has gathered more than 20 million developers working across 500,000 companies across the world, allowing them to streamline collaboration and simplify every step of API building.
One of Postman’s main advantages is its ease of use. It’s quick and painless to set up, while being reasonably priced at just $12 for the basic package. Postman allows for quickly sharing APIs and easily testing method calls. In fact, users can save collections and share them with their colleagues. It’s seen by many web developers as an essential tool, given its many features.
However, you may find the reporting capabilities somewhat lacking, making Postman more useful for quickly firing off requests locally or keeping an organized collection of the requests associated with your application. Once you move into automated testing—and especially continuous testing—using Postman may be labor intensive.
Developers that need to do performance testing should know that Postman is better suited for functional testing, while tools like JMeter are a better fit for performance tests.
While the sharing capabilities within a single team are great, there’s a lot left to be desired when it comes to sharing with non-workspace users. The complex nature of the platform results in some of the more advanced features practically being “hidden” unless you get far enough on the learning curve.
All things considered, Postman isn’t a bad choice for API testing. While the getting started guide provided by the platform isn’t as in-depth as you’d perhaps like, there’s plenty of information online that can help you master the tool.
Postman is also an excellent way to quickly set up mock APIs, allowing developers to design and test on the fly using the platform’s mock servers. Within Postman, you can make requests without a production API ready to go, as it’s easy to create mock servers based on API schemas. However, there’s no native way of seeding data based on production traffic.
Above all else, its teamwork features are definitely the platform’s main draw, as well as the ability to quickly create and execute API requests. If you need an easy way to share API requests or an easy alternative to running
curl commands, Postman is likely to be a good choice. But in terms of automating testing, you may find another tool more suitable.
Insomnia is a modern, user-friendly API testing tool mainly positioned as a competitor to Postman, meaning that it offers many of the same benefits as Postman, as well as many of the same limitations.
Insomnia’s biggest difference is how it approaches the user interface and user experience, focusing on providing a more streamlined and efficient experience. This proves to be very useful in a variety of ways, as users of Postman often find it too bloated whereas Insomnia is more minimalist and easy to get started with.
This does create some limitations for Insomnia, though. For example, both tools allow for request chaining; however, Insomnia requires you to run one request at a time, while Postman allows you to run an entire collection of requests at once.
In the context of API testing essentials covered previously, Insomnia is great at providing a simple and minimalist user interface, which most developers will find makes for a more efficient workflow. However, it has the same limitations of Postman, where it’s limited in terms of creating automated and continuous tests. Overall, Insomnia is a great tool depending on your use case.
Speedscale is one of the most recent additions to the API testing market, and also the one poised to be the most revolutionary, born out of a simple idea: making a tool that can ensure API updates won’t break services or apps.
With so many connection points between different applications, ensuring quality without a lot of manual work is almost impossible. Developers should be able to accelerate their release schedules by performing test automation with real traffic.
Speedscale is developed to focus on the API testing essentials outlined earlier, especially on using realistic data in a powerful yet efficient manner. Usually, developers would have to wait for a mistake to become apparent to users and then frantically work to fix it, but Speedscale allows them to see issues before release.
The platform builds QA automation without any scripting required and runs traffic-based API tests for performance, integration, chaos testing, etc. While other tools require scripting and observation, Speedscale allows developers to preview their app’s functionality under production load in mere seconds, allowing you to perform rapid tweaking and run isolated performance tests. Building mocks automatically from API traffic and showing the performance of an app in real-world conditions by simulating non-responsiveness, random latencies, and errors allow developers to obtain realistic insight into application behavior without relying on users.
Speedscale focuses solely on Kubernetes and by extension utilizes Kubernetes-specific features. For example, setting up the tool is done by installing an Operator into your cluster, after which you instrument your applications with the Speedscale sidecar proxy. This proxy takes care of recording incoming and outgoing requests—taking great care to prevent PII from being saved—and then stores it in their single-tenant, SOC2 Type 2 certified architecture, rather than requiring you to manage the data yourself. This approach allows you to record traffic in one environment and replay it in another—record in production, replay in development—without configuring direct access between the clusters.
After storing the traffic, users can explore and filter tests and mocks that are generated based on this traffic. Because all the data is stored and managed by Speedscale, and all actions such as traffic replay and automatic mock creation are handled by the Operator installed in your cluster, using Speedscale to integrate performance testing in your CI/CD pipelines is a simple task. One major benefit of Speedscale is how it integrates with other tools, even ones on this list. You can record traffic with Speedscale, then export it to a Postman collection.
SoapUI’s functionalities are useful for all web service developers, with excellent support from its own development team that’s quick to jump on any user-reported bugs.
There’s a free, open-source version of SoapUI and a premium one. That being said, even the free version can be useful for creating efficient web service mocks without a single line of code—all you need to do is point to the WSDL file. Still, this will be of limited use without the premium version, because most of today’s web services are REST and not SOAP, meaning they aren’t defined with a WSDL.
Apart from that, it’s quite powerful when it comes to complex test suites and simple mocks. Plus, you can use the Groovy script to write Java-ish code and manipulate responses and requests for your web services. You can even directly access a database and confirm the content if it’s been modified by a web service—an incredibly valuable tool for the testing and development of web services.
However, it’s not without its downsides—if you’re using the free version, there are still plenty of advanced features, but documentation is incredibly poor. You’ll basically need to go through various forum posts to learn to use the more powerful options provided by the Groovy script functionality. You’ll likely need to resort to trial and error and piece a lot of it together yourself.
Also, there seem to be some issues with the memory usage of SoapUI—it can grow to the point of crashing the program. You can alleviate this somewhat by allocating more memory to the program on startup and tinkering with logging levels, but it’s still a significant stability issue.
At the end of the day, SoapUI is a good choice if you want a simple mock service—or a complex one that’s not easy to use, and you’re ready to put time and energy into. However, if you do learn its ins and outs, SoapUI offers a lot of power and functionality for API testing.
Paw works extremely well with GraphQL, and its free tier offers more advanced options than most of its competitors, though it’s only compatible with Mac devices. It’s a full-fledged HTTP client for testing your APIs, with an extremely efficient interface that makes exporting API definitions, generating client code, inspecting server responses, and composing requests quite easy.
If you’re a macOS user, you’ll find that the likes of Postman or Insomnia don’t come close to Paw—especially in terms of GUI design and ease of use. One of the biggest issues with most other API testing tools is their cluttered UI (or, in the case of REST Assured, a lack thereof). However, Paw manages to sidestep this problem by providing an aesthetically pleasing and extremely well-thought-out user interface. Its variable system was made with great care, as was the JS-based plugin system that’s incredibly easy to use.
Paw allows you to quickly organize requests, sort by different parameters, make groups, etc. It has great support for S3, Basic Auth, and OAuth 1 and 2, and it can easily generate JWT tokens as well. Many developers appreciate the ease with which they can generate usable client code with Paw—you just need to import a cURL and modify the request, and PAW will help you generate the code for Python, Java, etc.
As we’ve mentioned, it’s quite easy to use—especially with a wide range of smart mouse and keyboard shortcuts. However, while its exclusive support for Mac devices means it’s quite focused and succinctly designed, this is also one of Paw’s biggest downsides.
The lack of support for Windows and Linux devices means that a lot of developers who prefer these operating systems will be left out of the Paw experience. Also, a big letdown is the lack of mock API features—meaning you’ll need another software from our list to do that. Finally, the extremely high per-user price also means it’s not the most affordable solution for all developers.
5 API Testing Methodologies
In this section, you’ll be presented with five methodologies that can be combined to create the most efficient pipeline, subsequently ensuring comprehensive coverage of your entire API.
At the core of validating API functionality is, well, functional testing. Examples of this are unit tests and integration testing, where you focus on the capabilities of the code execution rather than performance.
Functional testing will often be the first type of testing you implement, as these tests are the quickest and most efficient types of testing. The drawback of functional testing is that it gives you little to no information about the actual performance of code changes, which is why you’ll also need nonfunctional testing.
After verifying that all features work as expected, you can start looking into the performance and stability of those features. Nonfunctional tests can verify the capabilities and reactions of your application under various conditions.
Load testing can help verify your application’s ability to handle a given load. Stress testing can verify whether it can handle traffic spikes. Performance testing as a whole provides you with important and useful insights into your application’s performance—like measuring response times, throughput, and resource usage—before pushing any changes to production.
Functionality and performance are arguably the most important aspects of an application to test; however, security shouldn’t be ignored. To fully test the security of an application, you’ll likely want to hire a team of pen-testers. But, you can get far with some automated tools like Snyk.
Snyk is a tool that helps detect vulnerabilities by analyzing the code and its dependencies, then remediating it by automatically creating pull requests.
While it’s important to test new features, it’s perhaps even more crucial to verify that existing features aren’t experiencing decreased performance—also known as regression.
Implementing regression testing in your pipeline involves rerunning previously executed tests, to ensure that new changes aren’t negatively impacting already existing features. Or it can involve recording performance metrics such as transactions-per-second in production, and comparing them to the metrics collected during a load test.
Whichever way you choose to implement it, regression testing is an important aspect of maintaining high quality and reliability as your API evolves.
Production Traffic Replication
One of the most efficient ways to implement all the methodologies outlined above, is production traffic replication. This approach was mentioned briefly in an earlier section, but to reiterate: this approach involves capturing real-world traffic from your production environment and replaying it in your testing environment.
This is the only true way to test your API under realistic conditions, as you’ll consistently use the most up-to-date test data. Additionally, you’ll know for sure that you’re testing all parts of the API currently in use, given how traffic is captured directly from production.
What Is the Best API Testing Tool?
By combining the five testing methodologies, you can create a robust and efficient testing pipeline that realistically helps you identify and resolve issues, enhance performance, and ensure the security of your API. This will ultimately lead to an increased user experience and subsequently increased revenue.
The challenge now is to figure out how you can best and most effectively implement these methodologies, and choosing the right tool plays a major role in solving this challenge.
Once you’re done implementing these methodologies and your API is continuously being thoroughly tested, you can start looking into how this opens up the possibility of preview environments.