Stefania Gnesi

Davide Bacciu, Stefania Gnesi, Laura Semini

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.

Joanne M. Atlee, Stefania Gnesi

The workshop aims at reviewing the state of the art and the state of the practice in which formal methods and analysis approaches are currently applied in SPLE. This leads to a discussion of a research agenda for the extension of existing formal approaches and the development of new formal techniques for dealing with the particular needs of SPLE. To achieve the above objectives, the workshop is intended as a highly interactive event fostering discussion and initiating collaborations between the participants from both communities.