A Process for Complete Autonomous Software Display Validation and Testing (Using A Car-Cluster)


Malobika Roy Choudhury, SAP Labs India Pvt Lmt., India


Every product industry goes through the process of product validation before its release. Validation could be effortless or laborious depending upon the process. Here in this paper, a process is defined that can make the task-independent of constant monitoring. This method will not only make the work of test engineers easier it will also help the company meet stringent release deadlines with ease. The method explores how to complete visual validation of the display screen using deep learning and image processing. In the example, a method is discussed wrt a car-cluster display screen. The method breaks down the components of the screen then validates each component against its design and outputs a result predicting whether the displayed content is correct or incorrect. The models like You-Only-Live-Once, Machine Learning, Convolution Neural Networks-Conv2D, and image processing techniques like Hough circle/Hough lines are used to predict the accuracy of each display component. These sets of algorithms compile to provide consistent results throughout and are being currently used to generate results for the validation process.


Convolution Neural Networks, You-Only-Live-Once, display-validation.

Full Text  Volume 10, Number 11