Many modern user-intensive applications, such as Web applications, must satisfy the interaction requirements of thousands if not millions of users, which can be hardly fully understood at design time. Designing applications that meet user behaviors, by efficiently supporting the prevalent navigation patterns, and evolving with them requires new approaches that go beyond classic software engineering solutions. We present a novel approach that automates the acquisition of user-interaction requirements in an incremental and reflective way. Our solution builds upon inferring a set of probabilistic Markov models of the users' navigational behaviors, dynamically extracted from the interaction history given in the form of a log file. We annotate and analyze the inferred models to verify quantitative properties by means of probabilistic model checking. The paper investigates the advantages of the approach referring to a Web application currently in use

Ghezzi, C., Pezze', M., Sama, M., & Tamburrelli, G. (2014). Mining behavior models from user-intensive web applications.. In ICSE '14: Proceedings of the 2014 International Conference on Software Engineering (pp.277-287) [10.1145/2568225.2568234].

Mining behavior models from user-intensive web applications.

PEZZE', MAURO
;
2014

Abstract

Many modern user-intensive applications, such as Web applications, must satisfy the interaction requirements of thousands if not millions of users, which can be hardly fully understood at design time. Designing applications that meet user behaviors, by efficiently supporting the prevalent navigation patterns, and evolving with them requires new approaches that go beyond classic software engineering solutions. We present a novel approach that automates the acquisition of user-interaction requirements in an incremental and reflective way. Our solution builds upon inferring a set of probabilistic Markov models of the users' navigational behaviors, dynamically extracted from the interaction history given in the form of a log file. We annotate and analyze the inferred models to verify quantitative properties by means of probabilistic model checking. The paper investigates the advantages of the approach referring to a Web application currently in use
Si
paper
Web Application, Log Analysis, User Profiles, Markov Chains, Probabilistic Model Checking
English
ICSE International Conference of Software Engineering - May 31 June 7
978-1-4503-2756-5
Ghezzi, C., Pezze', M., Sama, M., & Tamburrelli, G. (2014). Mining behavior models from user-intensive web applications.. In ICSE '14: Proceedings of the 2014 International Conference on Software Engineering (pp.277-287) [10.1145/2568225.2568234].
Ghezzi, C; Pezze', M; Sama, M; Tamburrelli, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/103427
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