Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Davide Bacciu
(Dipartimento di Informatica, Università di Pisa)
Stefania Gnesi
(Istituto di Scienza e Tecnologie dell'Informazione, CNR)
Laura Semini
(Dipartimento di Informatica, Università di Pisa)

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage.

On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.

In Maurice H. ter Beek and Alberto Lluch Lafuente: Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems (WWV 2015), Oslo, Norway, 23rd June 2015, Electronic Proceedings in Theoretical Computer Science 188, pp. 75–83.
Published: 14th August 2015.

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