Applying Social Network Analysis to the Information in CVS Repositories :: essays research papers

Applying Social Network Analysis to the Information in CVS Repositories Abstract The huge quantities of data available in the CVS repositories of large, long-lived libre (free, open source) software projects, and the many interrelationships among those data offer opportunities for extracting large amounts of valuable information about their structure, evolution and internal processes. Unfortunately, the sheer volume of that information renders it almost unusable without applying methodologies which highlight the relevant information for a given aspect of the project. In this paper, we propose the use of a well known set of methodologies (social network analysis) for characterizing libre software projects, their evolution over time and their internal structure. In addition, we show how we have applied such methodologies to real cases, and extract some preliminary conclusions from that experience. Keywords: source code repositories, visualization techniques, complex networks, libre software engineering 1 Introduction The study and characterization of complex systems is an active research area, with many interesting open problems. Special attention has been paid recently to techniques based on network analysis, thanks to their power to capture some important characteristics and relationships. Network characterization is widely used in many scientific and technological disciplines, ranging from neurobiology [14] to computer networks [1] [3] or linguistics [9] (to mention just some examples). In this paper we apply this kind of analysis to software projects, using as a base the data available in their source code versioning repository (usually CVS). Fortunately, most large (both in code size and number of developers) libre (free, open source) software projects maintain such repositories, and grant public access to them. The information in the CVS repositories of libre software projects has been gathered and analyzed using several methodologies [12] [5], but still many other approaches are possible. Among them, we explore here how to apply some techniques already common in the traditional (social) network analysis. The proposed approach is based on considering either modules (usually CVS directories) or developers (commiters to the CVS) as vertices, and the number of common commits as the weight of the link between any two vertices (see section 3 for a more detailed definition). This way, we end up with a weighted graph which captures some relationships between developers or modules, in which characteristics as information flow or communities can be studied. There have been some other works analyzing social networks in the libre software world. [7] hypothesizes that the organization of libre software projects can be modeled as self-organizing social networks and shows that this seems to be true at least when studying SourceForge projects. [6] proposes also a sort of network analysis for libre software projects, but considering source dependencies between modules. Our approach explores how to apply those network analysis techniques in a more comprehensive and