This whitepaper presents an AI-driven solution for validating complex graphs and charts in software testing. It uses computer vision, deep learning, and data analytics to compare plotted data with CSV references automatically. The system identifies discrepancies in coordinates, symbols, and scales, highlighting errors with visual markers. A case study demonstrates significant time savings and accuracy improvements, reducing manual testing effort and ensuring data integrity.

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