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Neural networks are algorithms inspired by the human brain that learn from data to perform tasks like classification, regression, and decision-making. Unlike traditional software, where behavior is defined by explicit code, neural networks learn patterns and rules from training data. This learning-based approach introduces complexities in predictability and testability, making rigorous testing essential to prevent errors and biases.
n the fast-paced world of software development, the demand for high-quality applications delivered quickly has never been greater. Quality assurance (QA) is a cornerstone of this process, ensuring that software meets user expectations and operates without defects. Traditionally, QA has been a labor-intensive and time-consuming process. However, with the advent of artificial intelligence (AI), automated QA has emerged as a game-changer, bringing unprecedented efficiency, accuracy, and innovation to software testing.
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