Ever since the earthquake of April 6, 2009 hit the city of L’Aquila, Italy, the city has been facing major challenges in terms of social, physical, and economic reconstruction. The system of public urban mobility, the bus network, is no exception with its old bus fleet, non-user-friendly information, and poor scheduling. This paper argues that the public transportation system of L’Aquila could be improved towards smart mobility models without large infrastructure investment by leveraging the “infostructure” approach, digital technologies, open data, and open source software. This work presents the digitization and geo-referencing procedure, software architecture, and the web and mobile apps that have been developed to improve the information flow available to citizens and to increase the user-friendliness of the transportation system. Future research will seek to make use of the data and develop applications to perform a public transport accessibility analysis of major points of interest throughout the city.@en

Obtaining a good dataset to conduct empirical studies on the engineering of Android apps is an open challenge. To start tackling this challenge, we present AndroidTimeMachine, the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Encoded as a graph-based database, AndroidTimeMachine concerns 8,431 real open-source Android apps and contains: (i) metadata about the apps' GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions.@en

To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.@en