Generation of Realistic Navigation Paths for Web Site Testing Using RNN and GAN

Authors

  • Silvio Pavanetto Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy https://orcid.org/0000-0001-7301-2801
  • Marco Brambilla Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy https://orcid.org/0000-0002-8753-2434

DOI:

https://doi.org/10.13052/jwe1540-9589.20816

Keywords:

Web Engineering, Data Mining, Deep Learning, Recurrent Neural Networks, Generative Adversarial Networks, Testing

Abstract

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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Author Biographies

Silvio Pavanetto, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy

Silvio Pavanetto is a research fellow at Politecnico di Milano. His research interests include data science, social media monitoring, data-driven innovation, and big data analysis with particular attention to machine learning and deep learning techniques, applied on different scenarios and types of data, such as time series, images and text. In his two years of research he was the author of several papers published in international conferences and he worked on several research projects, also in collaboration with other European universities.

Marco Brambilla, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy

Marco Brambilla is a full professor at Politecnico di Milano. He manages several research projects and industrial innovation activities. His research interests include data science, software modeling languages, crowdsourcing, social media monitoring, data-driven innovation, and big data analysis. He has been visiting researcher at CISCO and UCSD, USA, and visiting professor at Dauphine University, Paris. He is the main author of the OMG standard IFML. He founded 3 startups and authored over 250 papers, 2 patents, and 5 books. He is editor and associate editor of various journals and he has been PC Chair of two editions of the ICWE Web Engineering Conference.

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Published

2021-11-21

Issue

Section

ICWE 2020