Why you should read the source if your shop sells custom apps on top of a framework. HN original post. Related: The costs of going it alone & Working with upstream: an interview with Lazslo Peter.

Our intention (Fran and me) when this paper first poped out from our heads was to foster debate on the best practices around a free software project. While at CartoLab, we presented the idea to Alberto; he encouraged us to work on it and gave the time and resources needed; also in the later stages he contributed to polish the trends and conclusions. I’m deeply grateful for all his patience and empathy.

I’m very proud of the work we have done: the first study of this kind in the GIS arena, and somehow a picture of 10 years of FOSS4G software development (for the desktop side). I hope the study is worth the effort and it continues to create debates on how to better work together.

Is it something we could extrapolate from the data there?

This indicator gives us some sense on how the leadership changed and how the knowledge transfer was done in every project. The paper elaborates a bit more the points of turnover and integration of new blood in the project (highly correlated with this indicator) with statistics of top 10 developers.

All that will give us some insights on every project:

GRASS

• The charts and data depict how a new generation took over the leadership from 2005 onwards. The process seems to be happened in a very organic way -in the sense that people grew its skills at a steady pace for a long time- and also deep to the roots: from the top10 only 4 out of 10 people continue collaborating with the project.
• The data also shows how the top3 represent half of the work in the project, which suggest that several developers are highly involved with no one having too much influence (actually, the top contributor during 2010 means 40% of work).

gvSIG

• The charts and data depict a highly distributed team with a high rate of turnover. Top3 is responsible for less than half of the contributions, being top10 around 60%. The change of leadership happened very quickly around 2007 and only 2 out of 10 contributors from top 10 kept working in 2010.
• Besides, the top10 shows a homogeneous involvement in terms of number of contributions, which may reflect that all of them had a similar role and impact in the development of gvSIG.

QGIS

• The charts and data depict a project dependent of its top3 with a contributions-friendly culture. Top3 activity means a hight rate of contributions over total but seems they have integrated well new blood as 9 out of 10 most active developers working in QGIS have started in different years and continue involved.
• Top10 people have different ratios of involvement, ranging from 6% to 50%, which may reflect the heterogeneity of its core developer base (from volunteers to full-time developers).

Is it something we could extrapolate from the data there?

This indicator is intended to give us some information on the patterns of behavior of contributors. Specifically, we can track how is a typical week for the core developers in every project: the timeline shows when the integration happened, don’t reflect the time in which the work was done; so it’s telling us the history of people with commit permissions, what we know as the leaders.

Let’s try to extract some information from there:

GRASS

• Internationalization: the hourly chart represents a gauss bell centered on 15h GMT, which in most European countries would be after lunch, being morning in the Americas. That could reflect that both continents represent the vast majority of core commiters. Nevertheless, the work is relatively well distributed along different hourly zones.
• Volunteers: the daily chart shows a light drop of work during the weekend, likely due to hired developers or people who likely make contributions mostly within their working hours. Nevertheless, there is still a high rate of contributions being integrated during weekend, which may be a sign of a well stablished volunteer base of core-developers.

gvSIG

• Internationalization: almost all the integration happens in a journey from Monday to Friday, with a hourly range from 09:00 to 20:00 GMT. That is strongly correlated to the hours of opening of a typical shop in Spain and reflects the nature on how the application was built in that period: led by a public body which contracted development to Spanish firms.
• Volunteers: seems that volunteer work in core was reaching to none, which reflects the original nature of the project in that period.

QGIS

• Internationalization: the hourly chart is nearly to a plain rate of contributions, which is a strong sign of a highly distributed leadership along the world. It’s even difficult to suggest which zones would be the prominent in terms of developers.
• Volunteers: the daily chart reflects a steady work along the week, with no signs of falling during the weekend, which may be related to a strong base of volunteers core commiters.

Havoc Pennington, A few thoughts on open projects. A lot of experience condensed in every word.

Open Source Projects and the meritocracy myth

Is it something we could extrapolate from the data there?

Certainly, not the number of features developed or bug fixes. It is even barely possible to compare activity between projects, as there are a high variability in terms of changesets: some people could send several little changesets and others just 1 big change, some project could have a special policy which affect the results (i.e.: make a commit formatting the code accoring to the style rules and other with the changes), etc. Some people could even argue that the language they are written in affects the number of changes (GRASS is written in C, gvSIG in Java and QGIS in C++) due to the libraries available or the semantics of every language. So, is it possible to find out something? Well, in my opinion, we can trace at least the following:

• the internal evolution of a project.
• how a project is doing in terms of adding new blood.

 So, let’s make again the exercise of finding out what’s happening here:

GRASS

• It calls the atention the curve of activity in the project: growth by periods (2001-2004 and 2005-2007) with local maximums in 2004 and 2007. Our hypothesis was that it was due to the way the project works: the developers here make changes both in the trunk and in the branch of the product to release (be it 6.4 or 6.5) at the same time, with a lot of changesets moved between both the trunk and the branches (so doing heavy backporting). In a recently conversation with Markus Neteler, he has explained me better how they work and I guess the rhythm we see in the graphics is due to that.
• In terms of number of developers, GRASS has showed a continuous growth until 2008; since then, the number of regular developers stabilizes.

gvSIG

• gvSIG shows an incredible high period of activity during 2006-2008 (4500 changesets by year and most that 30 people involved!). To understand the Gauss bell of activity, is needed to know the background of the project: gvSIG development has been led by contract, which means that all activities (planning, development, testing, etc) were led by the client needs who pay for it. Only recently, these processes have been opened to a broader community (firms and volunteers collaborating in the project within the gvSIG association). So, it makes sense that the beginnings had seen less activity (high phases of planing) and afterwards they got to agregate so many people in such a short period of time.
• But, in 2010 it suffered a sudden stop in development (only 233 changes to the codebase were made, while a pace of 4500 changes were made during previous years). This decreasing in activity is highly correlated to the number of developers involved. It’s hard to say why it happens: could it be due to the efforts were directed to gvSIG 2.0 development? could it be due to the reorganization in the project and the creation of gvSIG asociation? Well, few can we said at this respect with the data available, further research is required to determine that.

QGIS

The next one shows the same data (number of people writing and number of messages by month) for the developers mailinglists.

Is it something we could extrapolate from the data there?

Well, certainly not the user base. The data shyly introduce us the trends, not the real user base. The model we adopted to study the projects reflects just a part of the community -which is arguably the engine of project- but don’t take the data as the number of users for each project. For sure, each one of our favorite projects has more users than those participating in (these) mailinglists!

Anyway, here some food for thought:

• GRASS: it smoothly decreases in terms of number of messages as well as people writing, which happen within users and developers. The tendency is not clear though.
• gvSIG: the data shows a steadly increasing number of users participating in the mailinglists. On the other hand, although it is the project with more people suscribed to developer mailinglist, it shows the less activity of the three projects (in terms of # of messages in developer lists): few technical conversations seemed to happen through the mailinglists during that period.
• QGIS: according to the data, a clear growth exists in the community. In the period in study (3 years) the number of users and developers participating in mailinglists has been doubled!