Istituto di Scienza e Tecnologie dell'Informazione     
Bacciu D., Gnesi S., Semini L. Using a machine learning approach to implement and evaluate product line features. In: WWV 2015 - 11th International Workshop on Automated Specification and Verification of Web Systems (Oslo, Norway, 23 June 2015). Proceedings, vol. 188 pp. 75 - 83. Maurice H. ter Beek, Alberto Lluch-Lafuente (eds.). (Electronic Proceedings in Theoretical Computer Science, vol. 188). EPTCS, 2015.
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.
URL: http://eptcs.web.cse.unsw.edu.au/paper.cgi?WWV2015.8
DOI: 10.4204/EPTCS.188.8
Subject Software Product Line
Software Engineering
Machine learning

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