Learn how to use a model-based approach to test design and make software delivery faster and more predictable.
Understanding the flow logic and business rules for thoroughly testing an organization’s APIs can be overwhelming for software testers. Modern software delivery should be easier, faster and less chaotic. Bringing together the Parasoft ecosystem and the Curiosity Software model-based approach to test design creates the methodology testers need for effectively testing complex chains of API calls.
Running a simple functional test against an API seems reasonably achievable in theory, so long as you have a general understanding of your request and expected result. The problem is APIs chain applications and components together. As a result, pretty much every API test will be an integrated test.
Because of this, a successful test requires the ability to navigate complex chains of API calls, which makes test design complex. Further, it requires you to create the data to send in a request. Then there are bottlenecks that come from environmental constraints before different API versions are even considered.
For these reasons, model-based testing and synthetic test data generation combined are the perfect solution to the logical complexity of API testing.
The first challenge of API testing is identifying different routes that data can flow through complex chains of API calls. You’ll then need to identify which routes to traverse in testing. Also, the selection process must retain test coverage.
When you’re trying to formulate expected results manually, it destabilizes the reliability of API testing. Testers have to second guess how APIs should respond to many possible requests. In many cases, testers just run a test and make their best guess as to whether it’s passed. There’s no way to know confidently whether the right API test result was achieved.
You must also source the data combinations for your API tests. Many combinations are needed for adequate coverage, but they can’t usually be found in existing data sources. As a result, test data often undermines the agility and rigor of API testing.
You have to hand crank data for your tests when they use data drawn from production. This manual process is too slow to create API tests of sufficient coverage because of the many possible routes through API chains that can be tested.
APIs interact with many different components and you need access to each, whether in-house or third-party systems, to fire off requests and check responses. Components are frequently constrained or unavailable, which leads to API testing bottlenecks.
Rigorous API testing has to test against multiple versions of a request. As APIs change, connected applications continue to send old versions of a given request. You have to test against these versions, adjusting their tests, data and environments. However, they lack automated version control, leading to significant and wasteful duplicate effort.
Overcoming the complexity of testing complexity chains of API calls requires a systematic, automated approach. Model-based testing and test data management effectively address the logical complexity of APIs. Modeling makes rigorous API testing in short iterations totally possible.
Parasoft helps organizations continuously deliver quality software with its market-proven, integrated suite of automated software testing tools. Supporting the embedded, enterprise, and IoT markets, Parasoft’s technologies reduce the time, effort, and cost of delivering secure, reliable, and compliant software by integrating everything from deep code analysis and unit testing to web UI and API testing, plus service virtualization and complete code coverage, into the delivery pipeline. Bringing all this together, Parasoft’s award winning reporting and analytics dashboard delivers a centralized view of quality enabling organizations to deliver with confidence and succeed in today’s most strategic ecosystems and development initiatives — security, safety-critical, Agile, DevOps, and continuous testing.
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