Automated API Testing
Rigorously test fast-changing APIs within an iteration.
Test Modeller makes complex API testing simple, using a visual approach to generate rigorous API tests and data from easy-to-use flowcharts.
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Rigorous, automatically generating the smallest set of API tests needed to rigorously test every different combination of API call and data.
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As complex as your system, using a simple drag-and-drop approach to generate optimised tests for complex chains of APIs.
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Reactive to change, using central models to update automated tests and data when your APIs change, with full version control to test against every possible request.
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Structured and measurable, with expected results clearly and fully defined, and mathematical coverage measures to know exactly what is being tested.
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Faster, automating complex tasks like API test creation, test data definition, and the formulation of complete expected results.
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“Single pane of glass”, executing automated API tests across a range of proprietary and homegrown frameworks, as well as open source tools like REST Assured.
Prepare complex API's for every version of requests
With Test Modeller, you can generate up-to-date tests and data from easy-to-maintain flowcharts. Full version control allows you to test against every previous version of request users could make.
Slow and Manual API Testing
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API Testing with Test Modeller
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1. Testers Slowly and Manually Formulate Complex API Tests: A single API can request a vast range of data. QA must identify every combination of call and data needed for rigorous API testing, but they are faced with incomplete documentation and highly technical specification files. Slow and complex test design misses out most of the possible tests. |
1. Model Complex APIs as Easy-to-Use Flowcharts: Clear and precise flowcharts are built automatically from API documentation and existing tests. The visual flowcharts map all possible routes that data can flow through an API. Missing logic is quickly spotted and added, and rigorous tests are generated automatically for every possible end point. |
2. Expected Results Undermine Testing Confidence:
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2. Define Full and Accurate Expected Results Easily:
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3. Test Data Risks Non-Compliance and Undermines Testing Rigour:
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3. Specify “Just in Time” Test Data for Every Possible Test:
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4. Resource-Intensive Testing leaves API Chains Vulnerable:
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4. Generate the Smallest Set of API Tests Needed for Rigorous Testing:
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5. Slow and Erroneous Data Comparisons: Testers execute their tests, producing large quantities of complex responses. They must compare the resultant data slowly to poorly defined expected results, laboriously formulating run results. |
5. Rapid "Single Pane of Glass" Test Execution: API tests are executed across proprietary, homegrown, or open source frameworks. Responses are compared automatically to the expected results, with run results formulated automatically in a range of formats. |
6. Test Maintenance Leaves Previous Request Versions Untested: APIs change fast, but users continue to send old requests. QA teams must test against all possible versions, but lack version control and must update tests and data manually after every change. API testing falls behind, and insufficiently tested APIs return erroneous responses for old request versions. |
6. Rapidly Test Against Every Possible Version of Request: Updating the central model maintains all the API tests and data automatically. Full version control allows testers to quickly and simply roll-back to a previous version of their API tests, validating that the new API functions as it should when all version of API call are sent to it. |