A Practical Guide on Improving QA Testing with Gen AI

Generative Artificial Intelligence (Gen AI) is being integrated into Quality Assurance (QA) testing processes to increase efficiency, test execution speed, and defect identification. Gen AI tools are used for creating test data, analyzing logs, generating reports, and developing test cases. The most popular types of Gen AI models include autoregressive models, generative adversarial networks (GANS), transformer-based models, recurrent neural networks (RNNs), and variational autoencoders (VAEs). Gen AI is being used in QA testing for test data generation, test case development, test log analysis, test reporting, visual regression testing, testing edge cases, code generation, defect identification, and security testing. The benefits of integrating Gen AI into QA testing processes include comprehensive test coverage, augmented testing efficiency, on-demand reporting simplicity, streamlined defect analysis, accelerated automation mastery, and resource-efficient security and performance testing. Strategies for integrating Gen AI into testing processes involve analyzing issues with existing QA testing processes, matching them with the benefits of Gen AI, creating a pilot project with a dedicated team, providing training in selected Gen AI tools, selecting where to start based on priority, monitoring progress, and having human testers monitor the accuracy of tests.