Ulrichs blog - Debian

Debian Ireland Meetup Friday 17th of May

Wed, 15 May 2013 00:00:00 +0100

Thanks to Federico the Debian Irish User Group celebrates the Wheezy release with some pints this Friday (17.05.2013) at 8 at Mac Turcaill’s. For more information and a link to the pub have a look at the mailing list posting from Federico.

Oh and by the way: the Irish Debian Community officially launched last year, i.e. debian-dug-ie is #newinwheezy.

Visualizing DPL votes

Wed, 24 Apr 2013 00:00:00 +0100

Axel ‘XTaran’ Beckert recently asked in #debian-devel for a visualization of the Debian Project Leader Election results. Unfortunately there seems to be none, except the table in the mail. As I am currently trying to use ggplot2 for more things, I thought I would give it a try and convert the data into a csv file and process the data via R.

For converting the original data i used vim to do a little text processing and created a csv file. By running the following code we can create a nice looking simple graphic:

library("ggplot2")
votes <- read.csv("dpl-2013.csv")
votes2 <- melt(subset(votes, select=c("Year", "DDs", "unique")), id="Year")

p <- ggplot(votes2, aes(Year, value, group=variable, fill=variable)) + ylab('DD #')
p <- p + geom_point() + geom_line() + geom_area(position='identity')
p <- p + scale_fill_manual("Values", values=c(alpha('#d70a53', 0.5),
alpha("blue", 0.5)), breaks=c("DDs", "unique"), labels=c("Developer count", "Voters"))

print(p)

I also created a little script for automatically processing the csv file and creating a similar plot. Feel free to fork/clone extend this script.

Analyzing rc bug messages

Sun, 21 Apr 2013 00:00:00 +0100

Michael Stapelberg recently posted a blog post about looking into the number of Debian Developers actively working on RC bugs for the upcoming wheezy release.

In this blog post I analyze the data shared by Michael and provide the R commands used to generate the plots & findings. If you are interested into looking into the data yourself, but don’t like R, I suggest using ipython notebook + numpy instead.

Analysis

After parsing the data file we typically want to get an understanding of the data, by using summary(bugs) we get the minimum(1), median(5), mean(15.4), max(716) and quantiles of the data. This shows that the number of messages is wide-spread and a few people contribute a lot. To visualize the dispersion of the data we can create a box plot showing the range of messages:

As the first and third quantile are close together we can assume that the majority of the work is done by a few, especially since the second quantile is 5. This is supported by the histogram below, where the x axis is the number of recorded messages and y is the number of developers.

Top 10 contributors

The TOP 10 contributors, according to the dataset, are:

Lucas Nussbaum - 716 messages
Gregor Herrmann - 270 messages
Jakub Wilk - 270 messages
Andreas Beckmann - 225 messages
Julien Cristau - 205 messages
Cyril Brulebois - 169 messages
Moritz Muehlenhoff - 162 messages
Michael Biebl - 159 messages
Salvatore Bonaccorso - 158 messages
Christoph Egger - 142 messages

r commands

These are the commands used to generate the plots and information in this plot:

bugs <- read.csv("by-msg.csv")
summary(bugs)
boxplot(bugs$rcbugmsg, log='y', range=0, ylab="# bugs")
quantile(bugs$rcbugmsg)
0%  25%  50%  75% 100%
1    2    5   12  716

# create histogram
llibrary('ggplot2')
ggplot(bugs, aes(x=rcbugmsg)) + geom_histogram(binwidth=.5, colour="black", fill="black") + scale_x_sqrt()

top10 <- tail(bugs[order(bugs$rcbugmsg),], 10)
top10