According to H2K Infosys, manual creation, repetition, and analysis of tests is the basis of conventional QA processes for defect detection in software. However, QA Testing with AI refers to the application of artificial intelligence and machine learning to automate tests, defect predictions, improve test coverage optimization, and speed up releases. While conventional QA may be both lengthy and error-prone, using AI-based QA is beneficial for increasing efficiency and accuracy due to analysis of big data and adaptation to applications changes dynamically. Therefore, as the complexity of modern applications increases, it is important to leverage the potential of QA testing based on AI to ensure higher efficiency, quality, and cost-effectiveness.
