Best Practices for Data Driven API Testing
When we start thinking to apply data driven test procedures in API Testing few Data Driven testing approaches are very useful.
Ø Exhaustive application logic validation
Ø Faultlessness Performance Metrics
Ø Efficient technology stack utilization
Ø Integrate with Agile software delivery techniques
Ø Automation-ready tests
Simple best driven Practices in our Data Drive API Tests.
1. Use Pragmatic Data
2. Testing of Positive and Negative Outcomes
3. Use data to drive Dynamic Insistence
4. Groove API Responses/Replies
5. Reprocess Data Driven Functional Tests for Security and Performance.
Use Pragmatic Data
This may sound obvious, but the more closely your test data replicates the situations that the API will face in production, the more thorough and accurate your testing will be.
Starting with the source — the business procedures that your API was built to enable — is the best method to make sure your test data is realistic.
Make it a priority to understand the rationale for the API, as well as the data being provided to it, both in design and in practice.
It’s also crucial to keep in mind that data can have a variety of non-obvious interrelationships: certain input values may be dependent on other data sent to the API, and the same criteria may apply to returned data. You should be able to appropriately portray these relationships in your test data with the right tools.
Testing of Positive and Negative Outcome
Usually people largely think about monitoring positive replies from APIs: sending valid data should result in a successful server side activity and a response to that effect being sent to the API’s invoker.
But this is only the commencement; it’s also crucial to ensure that giving the API erroneous or otherwise invalid parameters results in a negative outcome, such as an error message or other indicator of a problem.
Furthermore, functional tests can be set up to gracefully handle error scenarios that would otherwise cause the test to stop.
This way of API evaluation minimizes the need for the tester to sift through the entire set of results in order to pinpoint a point of failure.
Instead, only examples in which a predicted positive result turned out to be negative — or vice versa — should be studied.
Use data to drive Dynamic Insistence
The rules that express the expected response from every given API call are known as assertions.
They are the primary quality metrics since they are used to determine whether an API is functioning according to its specifications. Many testers make the error of hard-coding these standards, which add to the API evaluation process’s maintenance overhead and brittleness.
Dynamic assertions, on the other hand, are adaptable and can change from one API call to the next.
Groove API Responses/Replies
Many testers become fixated on the success or failure of each API invocation, discarding the collection of results once their functional tests are completed.
That’s a pity, because API responses are extremely useful artifacts. Important history is lost if these test findings are not recorded.
When an API undergoes many changes and a new error is discovered during the regression testing process, determining which alteration caused the flaw can be a Herculean undertaking. Finding the exact time when the new problem originated — and fixing it — is considerably easier with the help of a library of stored API calls and responses.
Reprocess Data Driven Functional Tests for Security and Performance
Many firms use very unrealistic, narrowly focused performance and security tests, which are also hampered by limited sets of hard-coded test data.
Because it takes time and effort to build up a properly configured, adaptive data driven functional test, why not use it for more than one goal once you’ve made the investment?
Using a data-driven functional test to evaluate performance and security adds a healthy dose of reality to the process, and wonderful tools like ReadyAPI make it simple to do so.
