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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. 

Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35%) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35%) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localization allows SOSRepair to patch 24 (37%) defects, of which 16 (67%) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches.