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| # | |
| # 13-emse.R, 16 Jul 16 | |
| # | |
| # Data from: | |
| # Do time of day and developer experience affect commit bugginess? | |
| # Jon Eyolfson and Lin Tan and Patrick Lam | |
| # | |
| # Example from: | |
| # Evidence-based Software Engineering: based on the publicly available data | |
| # Derek M. Jones | |
| source("ESEUR_config.r") | |
| library("circular") | |
| plot_layout(2, 1) | |
| pal_col=rainbow(3) | |
| # id repository_id raw_author_id sha1 merge utc_time local_time | |
| commits=read.csv(paste0(ESEUR_dir, "time-series/commits/scc_commitbasicinformation.tsv.xz"), sep="\t", as.is=TRUE) | |
| commits$is_introducing= (commits$is_introducing == "t") | |
| commits$is_fixing= (commits$is_fixing == "t") | |
| fault_commits=subset(commits, is_introducing) | |
| basic_commits=subset(commits, !is_introducing) | |
| week_fault=subset(fault_commits, week_day < 5) | |
| week_basic=subset(basic_commits, week_day < 5) | |
| # plot(table(fault_commit$hour)) | |
| plot_commits=function(df, repo_str, col_str) | |
| { | |
| HoW=circular(df$hour, units="hours", rotation="clock") | |
| #plot(HoW, stack=TRUE, shrink=3, axes=FALSE, cex=0.01, col=col_str) | |
| rose.diag(HoW, bins=24, shrink=1.1, prop=3, axes=FALSE, col=col_str) | |
| axis.circular(at=circular(0:23, units="hours", rotation="clock")) | |
| text(0.8, 1, repo_str, cex=1.4) | |
| arrows.circular(mean(HoW), y=rho.circular(HoW), col=pal_col[2], lwd=3) | |
| # print(c(mean(HoW)[[1]], rho.circular(HoW))) | |
| lines(density(HoW, bw=30)) | |
| return(HoW) | |
| } | |
| F=plot_commits(week_fault, "Fault", pal_col[1]) | |
| B=plot_commits(week_basic, "non-Fault", pal_col[3]) | |
| # Two sample QQ plot from Circular statistics in R page 132-133 | |
| # Cleaned up a bit | |
| TwoSampleQQ = function(cdat1, cdat2) | |
| { | |
| n1 = length(cdat1) | |
| n2 = length(cdat2) | |
| nmin = min(n1, n2) | |
| nmax = max(n1, n2) | |
| cdatref = cdat1 | |
| cdatoth = cdat2 | |
| if (n2 < n1) | |
| { | |
| cdatref = cdat2 | |
| cdatoth = cdat1 | |
| } | |
| zref = sin(0.5*(cdatref-median.circular(cdatref))) | |
| szref = sort(zref) | |
| zoth = sin(0.5*(cdatoth-median.circular(cdatoth))) | |
| szoth = sort(zoth) | |
| szothred = as.vector(szoth[1+nmax*((1:nmin)-0.5)/nmin]) | |
| szreffin = as.vector(szref[1:nmin]) | |
| plot(szreffin, szothred, col="red", | |
| xlim=c(-1, 1), ylim=c(-1, 1), | |
| xlab = "Smaller sample", ylab = "Larger sample") | |
| xlim = c(-1, 1) | |
| ylim = c(-1, 1) | |
| lines(xlim, ylim, lty=2) | |
| } | |
| # F_t=circular(week_fault$hour*2*pi/24) | |
| # nF_t=circular(week_basic$hour*2*pi/24) | |
| # TwoSampleQQ(F_t, nF_t) | |