Abstract
A robust technique for generating web navigation logs could be fundamental for applications not yet released, since developers could evaluate their applications as if they were used by real clients. This could allow to test and improve the applications faster and with lower costs, especially with respect to the usability and interaction aspects. In this paper we propose the application of deep learning techniques, like recurrent neural networks (RNN) and generative adversarial neural networks (GAN), aimed at generating high-quality weblogs, which can be used for automated testing and improvement of Web sites even before their release.
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Notes
- 1.
A list of pages that represent the possible entry points for every navigation session. A probability to start the navigation with that page is associated with each one.
- 2.
The confidences are the probabilities for moving from a specific page to another, or, the probabilities for moving to a new page at a particular moment T, knowing the complete navigation path done from the beginning of the session (In this case, session means a portion of continuous time in which the user is browsing without leaving or interrupt the navigation.), until T.
- 3.
A list of mean times expressed in seconds that correspond to the quantity of time that users spend on that page on average.
- 4.
The graph representing the entire web site, where each page is associated with a list of possible subsequent pages.
- 5.
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Pavanetto, S., Brambilla, M. (2020). Generation of Realistic Navigation Paths for Web Site Testing Using Recurrent Neural Networks and Generative Adversarial Neural Networks. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science(), vol 12128. Springer, Cham. https://doi.org/10.1007/978-3-030-50578-3_17
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