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# | |
# residual-DAYLOC.R, 15 Oct 20 | |
# Data from: | |
# The {Linux} Kernel as a Case Study in Software Evolution | |
# Ayelet Israeli and Dror G. Feitelson | |
# | |
# Example from: | |
# Evidence-based Software Engineering: based on the publicly available data | |
# Derek M. Jones | |
# | |
# TAG Linux_evolution regression_residual | |
source("ESEUR_config.r") | |
# Need to get this plot to fit in the margin, along with the plot before it | |
plot_layout(2, 1, max_height=14) | |
par(mar=MAR_default-c(0.8, 0, 0.0, 0)) | |
pal_col=rainbow(3) | |
# Lines of code in each release | |
ll=read.csv(paste0(ESEUR_dir, "regression/Linux-LOC.csv.xz"), as.is=TRUE) | |
# Data of each release | |
ld=read.csv(paste0(ESEUR_dir, "regression/Linux-days.csv.xz"), as.is=TRUE) | |
loc_date=merge(ll, ld) | |
# Add column giving number of days since first release | |
loc_date$Release_date=as.Date(loc_date$Release_date, format="%d-%b-%Y") | |
start.date=loc_date$Release_date[1] | |
loc_date$Number_days=as.integer(difftime(loc_date$Release_date, | |
start.date, | |
units="days")) | |
# Order by days since first release | |
ld_ordered=loc_date[order(loc_date$Number_days), ] | |
# What is the latest version | |
n_Version=numeric_version(ld_ordered$Version) | |
# cummax does not work for numeric_version, so we | |
# have to track the latest version | |
greatest_version <<- n_Version[1] | |
keep_version=sapply(2:nrow(ld_ordered), | |
function(X) | |
{ | |
if (n_Version[X] > greatest_version) | |
{ | |
greatest_version <<- n_Version[X] | |
return(TRUE) | |
} | |
return(FALSE) | |
}) | |
latest_version=ld_ordered[c(TRUE, keep_version), ] | |
latest_version$MLOC=latest_version$LOC/1e6 | |
m1=glm(MLOC ~ Number_days, data=latest_version) | |
# m2=glm(MLOC ~ Number_days+I(Number_days^2), data=latest_version) | |
plot(m1, which=1, caption="", sub.caption="", col=pal_col[2]) | |
plot(latest_version$Number_days, latest_version$MLOC, col=pal_col[2], | |
cex.axis=1.4, cex.lab=1.4, | |
xaxs="i", yaxs="i", | |
xlab="Days", ylab="Total lines of code (MLOC)\n") | |
pred=predict(m1, type="response", se.fit=TRUE) | |
lines(latest_version$Number_days, pred$fit, col=pal_col[1]) | |
lines(loess.smooth(latest_version$Number_days, latest_version$MLOC, span=0.3), | |
col=pal_col[3]) | |
# |