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

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. Agile development teams in devops organizations aim to reduce feature cycle time, increase delivery frequencies, and ensure high-quality user experiences. The question is, how can they reduce risks and shift-left testing without creating new testing complexities, deployment bottlenecks, security gaps, or significant cost increases?