Generation of Realistic Navigation Paths for Web Site Testing Using RNN and GAN
Keywords:Web Engineering, Data Mining, Deep Learning, Recurrent Neural Networks, Generative Adversarial Networks, Testing
For applications that have not yet been launched, a reliable way for creating online navigation logs may be crucial, enabling developers to test their products as though they were being used by real users. This might lead to faster and lower-cost program testing and enhancement, especially in terms of usability and interaction. In this work we propose a method for using deep learning approaches such as recurrent neural networks (RNN) and generative adversarial neural networks (GANN) to produce high-quality weblogs. Eventually, we can utilize the created data for automated testing and improvement of Web sites prior to their release with the aid of model-driven development tools such as IFML Editor.
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