In this study we shed light on a unique and interdisciplinary domain, where music, technology, and human creativity intersect: music production software. Today software technologies are the predominant means of music production, with a vibrant ecosystem for commercial and open-source products. In this work we target VST plugins, the de-facto standard for developing and prototyping music production software. We analyze 15,847 data points over 299 GitHub repositories containing VST plugins. Our results include a systematic quantification of the (i) characteristics of open-source VST projects in terms of, e.g., duration, size, contributors, stars/watchers, licensing, (ii) most used technologies for developing VST plugins, and (iii) code quality and testing practices in VST projects. Our findings provide a comprehensive understanding of the current state of the practice in VST plugins development, highlighting successful projects, opportunities for improvement, and future research directions for software engineering researchers.

Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreover, robots are often battery-driven, meaning that even a small energy improvement can help reduce its energy footprint and increase its autonomy and user experience.In this paper, we study the Robot Operating System (ROS) ecosystem, the de-facto standard for developing and prototyping robotics software. We analyze 527 energy-related data points (including commits, pull-requests and issues on ROS-related repositories, ROS-related questions on StackOverflow, ROS Discourse, ROS Answers and the official ROS Wiki).Our results include a quantification of the interest of roboticists on software energy efficiency, 10 recurrent causes and 14 solutions of energy-related issues, and their implied trade-offs with respect to other quality attributes. Those contributions support roboticists and researchers towards having energy-efficient software in future robotics projects.

In today's world, reducing energy consumption should be the goal for any organization and any system, including robotics software systems. However, state of the practice in robotics software development focuses primarily on achieving functionality and performance, with minimal recognition of energy-efficiency as a driving software quality. The goal of this paper is to identify, synthesize, and empirically evaluate architectural tactics for energy-efficiency applied by practitioners in real robotics projects. Four tactics were identified by mining software repository techniques applied to the ROS ecosystem. The tactics were evaluated via experimentation on a real, commodity robotics system. Results show that the application of green architectural tactics tends to largely improve the energy-efficiency of the robot (7.9% energy savings when all tactics are applied) and that the movement strategy and the physical environment where the robot operates strongly influence how energy is consumed by the robot.

Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.

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.