Deep Learning (DL) is revolutionizing the face of many industries these days, such as computer vision, natural language processing, and machine translation, and it penetrates many science-driven products and technological companies.
In this post, Next Generation Automation share his experiences while building Deep Learning Solutions using GUI Testing for its Clients in North America region.
Current GUI testing talks more about Functional Testing focusing system external behavior or its elements and Structual Testing focusing in internal implementation like business work flows.
These methods are susceptible to changes and usually involve extensive automation efforts. Cross-screen testing, like in the case of desktop Web and mobile Web or mobile App testing, accentuates these risks and costs. Testing across multiple operating systems, devices, screen resolutions, and browser versions quickly becomes a huge challenge that is difficult to execute and govern.
Quality control measures, such as coverage-based or usage-based testing, address some of these uncertainties, but only to a certain degree, as it comes at a cost to the overall quality.
As product developers wrap-up GUI implementation, quality engineers begin breaking down the screen to its elements, identifying locators for each UI components and writing up large pieces of their code around asserting the elements’ aspects, such as dimension, position, and color, to make sure the GUI implementation matches the design. Even a slight design change or refactoring of product code could end up failing the regression suites and may involve significant re-work for QE to fix the automaton code.
Due to all this writing and maintaining test suites and scripts for multiple platforms take considerable time and effort and come at the risk of reducing the test scope.
Contemporary developments in Deep Learning space unleashes efficiencies in GUI testing and in the software lifecycle, potentially. A recent proof of concept, described below, proved this approach to be realistic and practical.
Deep Learning Technology
Deep Learning simulates the human way of finding errors or anomalies. Humans are driven by past experience and conditioning to make decisions. Machines with the proper application of training or conditioning can detect errors that surpass human precision.
Process of learning from training data and validating against test data is called modeling.When you model any application under test, first Algorithms take a set of training examples called as the training data and get learning insights about application. Once learning gets completed, it provided with test data to validate learning algorithm.
Complete process called Modelling, More robust your algorithm going to be more stable you get the predictions for any new scenario.
Neural Nets (NN)
A Neural Nets is a group of logically connected entities that transfer a signal from one end to another. Similar to brain cells or neurons that are connected to enable the human brain to achieve its cognitive powers, these logically connected entities are called perceptrons, which allow signals to move across the network to do formidable computations, for example, differentiating a lily from a lotus or understanding the different signals in the traffic.
These operations become possible when we expose our Neural Nets to a significant amount of data. A deep neural net (DNN) is an addition of multiple layers arranged in the order shown in figure above. This mathematical lattice is the core structure that drives complex business workflows.