How can you use machine learning for API testing?

Using ML in API testing is mostly concerned with test generation and test data quality. We can use ML to generate new tests for our APIs. One of the greatest features of using ML to produce test data is creating unknown scenario tests. It may even result in detecting a security breach with our generated test data. It is mostly advised to use common ML tools for your systems because humans have limitations when it comes to considering problems that can occur to a system.

API testing is quite challenging without Machine Learning or Artificial Intelligence since it involves understanding how APIs work and creating test cases and scenarios. Machine Learning in test automation allows you to analyze and create tests based on API activity and traffic.

Machine learning brings a new level of efficiency and accuracy to API testing. It automates the creation of test cases and analyzes results, which frees up human testers to focus on more complex tasks. It can also learn from past API usage to identify potential issues that might otherwise be missed, leading to a more robust and reliable API.

Machine learning can help with various aspects of API testing, anual effort of creating and maintaining test data and increase the test coverage and accuracy. Generating test cases: Machine learning can analyze the API behavior and structure and create test cases that cover different scenarios, edge cases, and error conditions. This can save time and resources for writing and executing test cases and ensure that the API meets the functional and non-functional requirements. Reporting and feedback: ML can monitor and evaluate the test results and provide feedback and recommendations for improving the API quality and performance. This can help identify and fix issues faster and enhance the test automation process.

Using machine learning for API testing can improve the efficiency and effectiveness of the testing process. Here are some ways to apply machine learning techniques to API testing: * Test case generation: Machine learning algorithms can analyze existing test cases and API documentation to generate new test cases automatically. * Anomaly detection: Machine learning algorithms can be trained to identify patterns in API response data and detect anomalies.

In my experience, testing of more and more use cases is now feasible thanks to newer AI tools like computer vision, NLP and generative AI. Example, one of the use case which we were not able to automate previously was to make sure that the PDF content was as per the expectations. ow with computer vision and ChatGPT we are able to automate this use case as well. It is now possible to get test cases and scripts based on a given brief of the use case. Going forward, the API test automation is going to get even easier by using action based framework, where common functional actions are pre defined and implemented, and natural language is used to orchestrate/call the right actions.

API testing is essentially a classification problem as there are only 2 outcomes: Pass or Fail. So using traditional supervised ML here might have very good results for well-defined test scenarios. However, if you are dealing with a complex app such as a CRM or ERP, you also need to verify the functional outcome of an integration test, and not just the 200 HTTP code. This is challenging when you have layers of nested dynamic classes, ids, divs and iframes in the web app. Companies also routinely change their apps to remain competitive, so the code needs to be routinely maintained otherwise it becomes obsolete . This is hard to automate, and tools like Gen AI in its current state can help improve developer productivity but not generalise.

Here are some AI tools for API testing: Applitools A tool for software testing, monitoring, and app visual management. It can be used for manual QA and automated tests for desktop, mobile, and web-based apps. Appvance IQ An AI-driven, unified test automation system. It uses AI scripting and codeless test creation for test creation. Parasoft SOAtest A tool that can automate API testing. It has features like configuring many end points and choosing the one on which you want the tests to be run.

GUI tests have a reputation for taking longer to release products. However, API testing has proven to be the faster solution, saving up to eight hours of your time. This gives you a great opportunity to focus on other core aspects of software development. And It’s best to check how well-built a car is from the inside instead of its paint. Automated API testing drills down to the layers beneath the UI to ensure databases are communicating correctly between one and another. Ultimately, there’s just greater coverage, where system components are checked all at once. And, better test coverage means improved quality software and satisfied users.?

Foremost benefit of ML based API testing is time and effort saving. Dependency on human experience in the domain and technology/API is greatly reduce once the business specifics and API documentation is ingested by the ML/AI. Any attrition will have lesser impact on the operations, throughput and quality which leads to opportunities to better optimize the people cost.

I'm regularly using Github copilot I estimate my work is at least 30% more effective I feel my work has changed from writing my code to reviewing the machine's code It is like the revolution of WAZE after being used to read maps It'll take me much time to navigate with a map again And I'm sure that if co-pilot is taken from me it will take me time to resume to my previous productivity

It is a challenge to sell an ML based testing to a compliance officer and senior management especially if it is used to certify the quality of a product. It takes time to build the trust and it may not get full trust to completely rely on the ML testing alone.

Challenges such as the need for extensive, high-quality training data to develop reliable models, which can be labor-intensive to collect and maintain. Models also require continuous retraining to adapt to evolving APIs. Integrating ML with existing testing frameworks involves significant initial setup and technical expertise. Interpretability is another hurdle; understanding why an ML model flags an issue can be non-trivial (Deep analysis). False positives and negatives generated by ML need careful scrutiny to avoid misdirected testing efforts. Ensuring that ML models do not overfit to specific patterns and are generalized enough to handle a variety of test scenarios is crucial for the successful application of ML in API testing.

Trust specific challenges can be overcome by: The output and process needs to be easily explainable to have a concurrence from all stakeholders. The risk needs to thoroughly understood along with the likelihood of the risk materializing and its impact. Build enough checks/logs in the process and automation to allow for easy verification/audit of results. Start small and then expand to more critical and sensitive use cases once trust is built.

Remember that you don't want to build tests that pass with your code, you want to build code that passes your tests. LLMs are a fantastic tool to generate test cases, but it's important to not use them blindly based on an existing codebase. Instead, explain the behaviour you want, and the test cases, and use the AI to then generate tests based on those instructions. TDD is now more important than ever.