Appendices, data sets, and R scripts for:
"Adding Sparkle to Social Coding: An Empirical Study of Repository Badges in the npm Ecosystem."
Asher Trockman, Shurui Zhou, Christian Kästner, and Bogdan Vasilescu.
In Proceedings of the International Conference on Software Engineering, ICSE, ACM (2018).
You can run the R scripts on the CSV files in the repository to estimate the different regression models we report on in the paper.
Make sure you have R installed. We tested this on R 3.3.2.
We require the following dependencies: lme4, lmerTest, MuMIn, car, MASS, effects, effsize, sqldf, MASS, pROC, plyr, beanplot, RColorBrewer, ggplot2.
It may be necessary to change the Pandoc environment variable in make.R: the required path can be found by running Sys.getenv("RSTUDIO_PANDOC") in RStudio. If you are using OS X with an existing RStudio installation, this step is probably not needed.
You can run make from the root folder to generate appendix.pdf.
You can also run make.R directly from R by setting the working directory to this repository with setwd('/path') and running source('make.R').
If you want to change something and rebuild the appendix, it is most efficient to run source('make.R') from an existing R session. The script will detect that the data has already been loaded into memory and processed, preventing this time-consuming step from happening again.
Some data sets have been truncated to to meet GitHub's file size limit.
data/timeseries_freshness.csv: reduced from 3,294,725 to 47,400 rows, since we only considered projects with non-zero freshness scores in our longitudinal analysis.data/master_info.csv: removed unnecessary columns such as license and author information, which were not used in our study.