Solving It

Prodapt designed a three-pronged solution to help the company spot visual bugs and accelerate timelines. A Natural Language Processing (NLP) based evaluator would prioritize different test cases based on the degree of importance. The program gets textual inputs encapsulating what the test case is about – for example, the webpage where a customer places the order and moves ahead for the payment. Using a Machine Learning (ML) algorithm to determine the degree of importance, the evaluator produces a pecking order of test cases.

After the evaluator throws up the results, a computer vision (CV)-based tool analyses the user interface for visual bugs. Prodapt’s solution offers the flexibility to tweak the degree to which the interface can differ from the original sketch. Sometimes, testers just need to ensure all the components are in place. Other times, the UI needs to be an exact replication of the design – a pixel-to-pixel comparison where not an icon or word falls out of line. The CV tool allows for varying degrees of deviation from design, for several cases including the ones where dynamic/movable content is part of the UI.

Once the tool screens out the bugs, the defects are numbered, labeled, and shared with the developers. The defects are reviewed by an assigned human expert before generating a report to help teams detect them easier the next time.