Graphs are valuable interactive tools for comparing, categorizing, and visualizing data. In the Software Testing Life Cycle, complex graphs can make manual validation challenging for testers. Various tools exist to validate graphs against each other or compare them with external data sources. Some situations require multiple computations on external data to ensure accurate graph validation.

Our latest whitepaper discusses the complexities of graph validations, particularly for scatter plots, due to overlapping data points, which make manual validation difficult. It proposes an automated solution using Computer Vision and deep learning to validate complex graph contents against defined criteria using reference files. This automation aims to reduce manual efforts and improve testing cycle turnaround time.