Wed Jan 29, 2003
I’ve been working on a a paper [pdf] about Open Source Software development with Alan Schussman, a graduate student in my department. It’s still in a very early state—- we’re really just kicking around a few ideas—- but some of the findings are interesting. The basic idea is to try to look at the social structure of the OSS development community a bit more closely. Here’s a neat picture of the basic finding, which is that OSS projects are fantastically skewed on several different measures of activity. (They have ‘power law’ type distributions.) We’re not the first people to notice this by any means, though I don’t know of other work that looks at as many projects as we do, and Internet-related research on power laws has focused on the topology of the Internet rather than the organization of software development communities. The working paper has much more by way of context, prior research and speculation about what explains this.
Here’s the abstract.
The Ecology of Open Source Software Development.
Kieran Healy and Alan Schussman.
University of Arizona.
Abstract: Open Source Software (OSS) is an innovative method of developing software applications that has been very successful over the past eight to ten years. A number of theories have emerged to explain its success, mainly from economics and law. We analyze a very large sample of OSS projects and find striking patterns in the overall structure of the development community. The distribution of projects on a range of activity measures is spectacularly skewed, with only a relatively tiny number of projects showing evidence of the strong collaborative activity which is supposed to characterize OSS. Our findings are consistent with prior, smaller-scale empirical research. We argue that these find-ings pose problems for the dominant accounts of OSS. We suggest that the gulf between active and inactive projects may be explained by social-structural features of the community which have received little attention in the existing literature. We suggest some hypotheses that might better predict the observed ecology of projects.