In the digital world, handling extensive quantitative data can be difficult due to its inherent complexity. Graphs play a vital role in visualizing quantitative data, simplifying the process of comprehension for the tester using various types of charts.
Validating graphs — which is required when comparing them or cross-referencing them with external data — plays a crucial role in providing the visual aspects of the data to users and testers. However, manual validation of these graphs can be laborious and time-consuming — their intricate nature makes it difficult to identify differences and may lead to errors.
In this whitepaper, we propose a solution to eliminate the challenges in automating graph validation using AI. This approach allows us to validate all the contents of the graph, including colors, shades, boundaries and text.
