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3 ways to use data, analytics, and machine learning in test automation

October 18, 2021

Via: InfoWorld

Just 10 years ago, most application development testing strategies focused on unit testing for validating business logic, manual test cases to certify user experiences, and separate load testing scripts to confirm performance and scalability. The development and release of features were relatively slow compared to today’s development capabilities built on cloud infrastructure, microservice architectures, continuous integration and continuous delivery (CI/CD) automations, and continuous testing capabilities.

Furthermore, many applications are developed today by configuring software as a service (SaaS) or building low-code and no-code applications that also require testing the underlying business flows and processes.

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