Background: Contemporary software development relies heavily on reusing already implemented functionality, usually in the form of packages. Aims: We aim to shed light on developers' preferences when selecting packages in R language. Method: To do that, we create and administer a survey to over 1000 developers who have added one of two common dataframe enhancement libraries in R to their projects: data.table or tidyr. We design a questionnaire using the Social Contagion Theory (SCT) following prior work on technology adoption and ensure that key dimensions affecting developer choice are considered. Results: Of the 1085 developers we contacted, 803 completed the survey asking them to prioritize various factors known to affect developer perceptions of package quality and to provide their background. Most developers self-identified as data scientists with two to five years of work experience. We found significant differences between the preferences of developers who chose data.table and tidyr. Surprisingly, package reputation based on easy-to-see measures, such as the number of stars on GitHub, was not an important factor for either group. Conclusions: Our findings demonstrate the inherently social nature of package adoption. They can help design future studies on how different populations of developers make decisions on which software packages to use in their projects. Finally, package developers and maintainers can benefit by better understanding the prime concerns of the users of their packages.

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.