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Over the past eleven years, the Robot Operating System (ROS), has grown from a small research project into the most popular framework for robotics development. Composed of packages released on the Rosdistro package manager, ROS aims to simplify development by providing reusable libraries, tools and conventions for building a robot. Still, developing a complete robot is a difficult task that involves bridging many technical disciplines. Experts who create computer vision packages, for instance, may need to rely on software designed by mechanical engineers to implement motor control. As building a robot requires domain expertise in software, mechanical, and electrical engineering, as well as artificial intelligence and robotics, ROS faces knowledge based barriers to collaboration. In this paper, we examine how the necessity of domain specific knowledge impacts the open source collaboration model. We create a comprehensive corpus of package metadata and dependencies over three years in the ROS ecosystem, analyze how collaboration is structured, and study the dependency network evolution. We find that the most widely used ROS packages belong to a small cluster of foundational working groups (FWGs), each organized around a different domain in robotics. We show that the FWGs are growing at a slower rate than the rest of the ecosystem, in terms of their membership and number of packages, yet the number of dependencies on FWGs is increasing at a faster rate. In addition, we mined all ROS packages on GitHub, and showed that 82% rely exclusively on functionality provided by FWGs. Finally, we investigate these highly influential groups and describe the unique model of collaboration they support in ROS. 

Search-based automatic program repair has shown promise in reducing the cost of defects in real-world software. However, to date, such techniques have typically been most successful when constructing short or single-edit repairs. This is true even when techniques make use of heuristic search strategies, like genetic programming, that in principle support the construction of patches of arbitrary length. One key reason is that the fitness function traditionally depends entirely on test cases, which are poor at identifying partially correct solutions and lead to a fitness landscape with many plateaus. We propose a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariants over intermediate behavior. Our early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.