Adaptive Testing
Adaptive testing is a software testing methodology that dynamically adjusts test cases, data, or strategies based on real-time feedback from the system under test, such as code changes, previous test results, or risk assessments. It aims to optimize testing efficiency by focusing resources on high-risk or frequently changing areas, often using machine learning or rule-based algorithms to guide decisions. This approach contrasts with static, pre-defined test suites, enabling more responsive and intelligent quality assurance processes.
Developers should learn adaptive testing to improve test coverage and reduce time-to-market in agile or continuous integration/continuous deployment (CI/CD) environments, where frequent code changes require rapid feedback. It is particularly useful for large-scale applications with complex dependencies, as it helps prioritize testing on critical components or recently modified code, enhancing defect detection while minimizing redundant tests. This methodology supports data-driven decision-making in testing, making it valuable for teams aiming to automate and optimize their QA workflows.