Context The R programming language has a huge and active community, especially in the area of statistical computing. Its interpreted nature allows for several interesting constructs, like the manipulation of functions at run-time, that hinder the static analysis of R programs. At the same time, there is a lack of existing research regarding how these features, or even the R language as a whole are used in practice. Objective In this paper, we conduct a large-scale, static analysis of more than 50 million lines of real-world R programs and packages to identify their characteristics and the features that are actually used. Moreover, we compare the similarities and differences between the scripts of R users and the implementations of package authors. We provide insights for static analysis tools like the lintr package as well as potential interpreter optimizations and uncover areas for future research. Method We analyze 4 230 R scripts submitted alongside publications and the sources of 19 450 CRAN packages for over 350 000 R files, collecting and summarizing quantitative information for features of interest. Results We find a high frequency of name-based indexing operations, assignments, and loops, but a low frequency for most of R's reflective functions. Furthermore, we find neither testing functions nor many calls to R's foreign function interface (FFI) in the publication submissions. Conclusion R scripts and package sources differ, for example, in their size, the way they include other packages, and their usage of R's reflective capabilities. We provide features that are used frequently and should be prioritized by static analysis tools, like operator assignments, function calls, and certain reflective functions like load.

Self-adaptive cloud systems dynamically adapt their configuration to achieve Service Level Objectives (SLOs), for example, to ensure a response time below a certain threshold, in changing environments. However, understanding these recon-figurations is challenging. To address this issue, we introduce ADVISE, a visualization tool for reconfigurations in self-adaptive systems. Our adaption approach leverages the Palladio ecosystem to predict system performance and plan optimal reconfigurations by exploring the state space. The optimal path in the state space graph, determined by a utility value based on the SLOs, represents the ideal policy. Our tool provides real-time visualizations of performance predictions and reconfiguration plans aiding developers in managing and optimizing system behavior effectively. We enable both the analysis of individual states and the examination of paths within the state space graph, enabling users to analyze not only single adaptation decisions but also the overall reconfiguration behavior. Video URL: [1]