Abstract
Robots are being increasingly used in safety-critical contexts, such as transportation and health. The need for flexible behavior in these contexts, due to human interaction factors or unstructured operating environments, led to a transition from hardware-to software-based safety mechanisms in robotic systems, whose reliability and quality is imperative to guarantee. Source code static analysis is a key component in formal software verification. It consists on inspecting code, often using automated tools, to determine a set of relevant properties that are known to influence the occurrence of defects in the final product. This paper presents HAROS, a generic, plug-in-driven, framework to evaluate code quality, through static analysis, in the context of the Robot Operating System (ROS), one of the most widely used robotic middleware. This tool (equipped with plug-ins for computing metrics and conformance to coding standards) was applied to several publicly available ROS repositories, whose results are also reported in the paper, thus providing a first overview of the internal quality of the software being developed in this community.

Abstract
The Robot Operating System (ROS) is nowadays one of the most popular frameworks for developing robotic applications. To ensure the (much needed) dependability and safety of such applications we forecast an increasing demand for ROS-specific coding standards, static analyzers, and tools alike. Unfortunately, the development of such standards and tools can be hampered by ROS modularity and configurability, namely the substantial number of primitives (and respective variants) that must, in principle, be considered. To quantify the severity of this problem, we have mined a large number of existing ROS packages to understand how its primitives are used in practice, and to determine which combinations of primitives are most popular. This paper presents and discusses the results of this study, and hopefully provides some guidance for future standardization efforts and tool developers.

Abstract
The Robot Operating System (ROS) is an open source framework for the development of robotic software, in which a typical system consists of multiple processes communicating under a publisher-subscriber architecture. A great deal of development time goes into orchestration and making sure that the communication interfaces comply with the expected contracts (e.g. receiving a message leads to the publication of another message). Orchestration mistakes are only detected during runtime, stressing the importance of component and integration testing in the verification process. Property-based Testing is fitting in this context, since it is based on the specification of contracts and treats tested components as black boxes, but there is no support for it in ROS. In this paper, we present a first approach towards automatic generation of test scripts for property-based testing of various configurations of a ROS system.

Abstract
The Robot Operating System (ROS) is one of the most popular open source robotic frameworks, and has contributed significantly to the fast development of robotics. Even though ROS provides many ready-made components, a robotic system is inherently complex, in particular regarding the architecture and orchestration of such components. Availability and analysis of a system's architecture at compile time is fundamental to ease comprehension and development of higher-quality software. However, ROS developers have to overcome this complexity relying mostly on testing and runtime visualisers. This work aims to enhance static-time support by proposing, firstly, a metamodel to describe the software architecture of ROS systems (the ROS Computation Graph) and, secondly, model extraction and visualisation tools for such architectural models. The provided tools allow users to specify custom-made queries over these models, enabling the static verification of relevant properties that had to be (manually) checked at runtime before.

Abstract
Modern industry is shifting towards flexible, advanced robotic systems to meet the increasing demand for custom-made products with low manufacturing costs and to promote a collaborative environment for humans and robots. As a consequence of this industrial revolution, some traditional, mechanical- and hardware-based safety mechanisms are discarded in favour of a safer, more dependable robot software. This work presents a case study of assessing and improving the internal quality of a European research mobile manipulator, operating in a real industrial environment, using modern static analysis tools geared for robotic software. Following an iterative approach, we managed to fix about 90% of the reported issues, resulting in code that is easier to use and maintain.

Abstract
In principle, Model-Driven Engineering (MDE) addresses central aspects of robotics software development. Domain experts could leverage the expressiveness of models; implementation details over different hardware could be handled by automatic code generation. In practice, most evidence points to manual code development as the norm, despite several MDE efforts in robotics. Possible reasons for this disconnect are the wide ranges of applications and target platforms making all-encompassing MDE IDEs hard to develop and maintain, with developers reverting to writing code manually. Acknowledging this, and given the opportunity to leverage a large corpus of open-source software widely adopted by the robotics community, we pursue modeling as a complement, rather than an alternative, to manually written code. Our previous work introduced metamodels to describe components, their interactions, and their resulting composition, as inspired by, but not limited to, the de-facto standard Robot Operating System (ROS). In this paper we put such metamodels into use through two contributions [1]. First, we automate the generation of models from manually written artifacts through extraction from source code and runtime system monitoring. Second, we make available an easy-to-use web infrastructure to perform the extraction, together with a growing database of models so generated. Our aim with this tooling, publicly available both as-a-service and as source code, is to lower the MDE barrier for practitioners and leverage models to 1) improve the understanding of manually written code; 2) perform correctness checks; and 3) systematize the definition and adoption of best practices through large-scale generation of models from existing code. A comprehensive example is provided as a walk-through for robotics software practitioners.