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API testing is important. It helps with the detection of flaws in code, improves code quality, and enables developers to make changes more quickly while remaining confident that they have not broken existing behavior. API testing can benefit greatly from automation and artificial intelligence. Many products use automation in API testing, but the majority of firms have yet to realize the potential that AI and machine learning have for improving 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 may 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 created tests are more beneficial in these circumstances since the developer has a better understanding of the API usage. Adding intelligence provides an excellent opportunity to improve automated testing to work with business logic – for example, users will place an item in their online shopping cart before being taken to the page that requires an address, so testing an API with an address but no items are a waste of time. Intelligent automated testing could provide a dynamic set of input values that make sense, allowing for a more comprehensive evaluation of the API’s architecture and more confident results.
Manually creating new API test cases might be time-consuming. This can be sped up by creating tests, but developers can only rely on this if the generated tests are of good quality.
Semantic and syntactic awareness, or training an intelligent algorithm to grasp essential business or domain entities such as a ‘customer,’ ’email,’ or ‘invoice,’ and how to generate data from them, is one technique to increase the quality of generated tests. By directing it to existing tests, APIs, and business rules, it should be able to ‘learn’ from them and eventually get better at generating tests with minimal developer input.
Routine tasks can be identified and automated to drastically reduce a tester’s burden. The use of an algorithm to examine an API specification and determine the dependencies allows the machine to do routine setup and teardown operations. For instance, if a bookstore has an API for orders, 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 handles those activities and then cleans up and deletes them after the test. As an algorithm becomes more familiar with the company’s API structures, it can generate additional setup and takedown jobs.
API testing is more effective when realistic data, reflective of real-world production situations, is used. Because of the risk of revealing sensitive data, creating tests from production data must be done carefully. Creating real-world usable tests at scale is challenging without automation because of the high labor cost of combing through mounds of data, evaluating what is relevant, and cleaning the data of sensitive variables.
A recent update to the IBM Cloud Pak for Integration Test and Monitor uses artificial intelligence to analyze API workloads in both production and test settings, detecting how APIs are invoked in each. This analysis enables it to detect real-world production API scenarios that aren’t effectively replicated in the existing test suite and automatically develop tests to close the gap.
Allowing an algorithm to analyze millions of production API calls efficiently means that production staff will only need to review and approve the intelligently generated tests. This is a very successful 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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