API testing is critical. It aids with the detection of code problems, improves code quality, and allows developers to make changes more quickly while remaining confident that they will not break existing functionality. API testing can benefit greatly from automation and artificial intelligence. Many products use API testing automation, but the majority of firms have yet to realize the benefits of AI and machine learning in testing. As the future of API testing involves more AI and automation, IBM believes there are a few critical skills to keep an eye on.
A developer might use code to create random inputs for each field in basic automated testing. Many of those tests will be ineffective because they are repetitious or do not correspond to the application’s intended business purpose. Manually developed tests are more valuable in these situations because the developer has a greater understanding of how the API is used.
Adding intelligence to automated testing allows it to integrate with business logic – for example, customers will add an item to their online shopping basket before being directed to the page that requires an address, so testing an API with an address but no items is a waste of time. Intelligent automated testing could generate a dynamic set of input values that make sense and are a broader test of the API’s design with more confident results.
Manually creating new API test cases might be time-consuming. Test generation can help, but developers can only rely on it if the created tests are of good quality.
One way to improve the quality of generated tests is semantic and syntactic awareness – that is, training an intelligent algorithm to understand key business or domain entities such as a ‘customer’, ’email’, or ‘invoice’ – and how to generate data from them. It should be able to ‘learn’ from current tests, APIs, and business rules, and become better at creating tests with less developer input in the future.
Identifying and automating typical operations can drastically reduce a tester’s burden. The machine may perform routine setup and takedown chores by using an algorithm to examine an API definition and determine the dependencies. If a bookshop has an API for ordering, for example, the AI can set up the scaffolding and create the test prerequisites. If a tester wants to generate a book and a customer before placing an order, the AI does those chores, which are then cleaned up and removed after the test is completed. As an algorithm becomes more familiar with the company’s API structures, it will be able to produce additional setup and teardown jobs.
Identifying and automating common tasks can significantly reduce a tester’s workload. The computer may conduct routine setup and takedown tasks by examining an API definition and determining dependencies via an algorithm. If a bookshop has an API for ordering, for example, the AI can set up the scaffolding and create the test prerequisites. If a tester wants to create a book and a customer before placing a purchase, the AI will take care of those tasks, which will then be cleaned up and removed after the test is through. As an algorithm gains a better understanding of the company’s API structures, it will be able to generate more setup and teardown jobs.
A new feature in IBM Cloud Pak for Integration Test and Monitor employs artificial intelligence to analyze API workloads in both production and test settings, detecting how APIs are used in each. This analysis enables it to discover real-world production API scenarios that aren’t effectively replicated in the existing test suite and produce automated tests to fill the gap.
Allowing an algorithm to efficiently examine millions of production API calls means that all that’s left for production personnel to do is review and approve the tests smartly. This is a highly effective method of boosting test coverage in the most impactful way possible, as it prioritizes resolving testing gaps based on how users interact with APIs in the real world.
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