Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers’ investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source version control repositories (containing over 4 billion unique versions) to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers’ software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language:tidyanddata.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworksAngularandReact, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software.

Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are tens of millions of projects in the periphery interconnected through technical dependencies, code sharing, or knowledge flows? To answer such questions we a) create a very large and frequently updated collection of version control data for FLOSS projects named World of Code (WoC) and b) provide basic tools for conducting research that depends on measuring interdependencies among all FLOSS projects. Our current WoC implementation is capable of being updated on a monthly basis and contains over 12B git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.