This paper introduces and expands on previous work on a collaborative project, called FLOSSmole (formerly OSSmole), designed to gather, share and store comparable data and analyses of free and open source software development for academic research. The project draws on the ongoing collection and analysis efforts of many research groups, reducing duplication, and promoting compatibility both across sources of FLOSS data and across research groups and analyses. The paper outlines current difficulties with the current typical quantitative FLOSS research process and uses these to develop requirements and presents the design of the system. 2

SourceForge provides abundant accessible data from Open Source Software development projects, making it an attractive data source for software engineering research. However it is not without theoretical peril and practical pitfalls. In this paper, we outline practical lessons gained from our spidering, parsing and analysis of SourceForge data.

Libre (free / open source) software is a good example of global software development. Thousands of projects, in a wide range of domains which involve hundreds of thousands of developers and contributors from all around the world. Some large (both in size and developer community) libre software projects have shown evidence of producing code with complete functionality and fast evolution (with linear or superlinear growth), while maintaining low defect density. Many companies are exploring how to benefit from this situation, considering several approaches related to libre software development. For instance, some of them have hired full-time developers, focusing their work on some libre software projects they consider strategic. However, before joining the core development team of the project, these hired developers have to follow a process of software comprehension, and get used to the rules and communication mechanisms used in the project. We were interested in the differences between this case and that of volunteer developers working in the same project, Therefore, we studied the duration and basic characteristics of this joining process for the developers of GNOME (a well known, large, libre software project). In our analysis, we have found two groups with clearly different joining patterns. Moreover, we have related those patterns to the different behaviors of volunteer and hired developers: volunteers tend to follow a step-by-step joining process, while hired developers usually experience a “sudden ” integration. Some reasons for this different behavior are also discussed.

There is a natural match between studies of online communities and social network analysis (SNA). Information Systems research, in particular, has drawn heavily on the growing data sources available as a by-product of increased online social interaction, conducting social network analyses with this “trace data”. However, this type of data has properties that are distinct from the data upon which researchers in Sociology and Anthropology have typically developed social network measures and their interpretations. This paper examines validity issues deriving from the use of trace data in IS studies of online communities, arguing that more attention needs to be paid to the chain of logic linking data collection, construct development, operationalization and measure interpretation. Four issues are elaborated and illustrated from the literature: 1) unexpected reliability concerns, 2) the need to argue and test the link from ‘found data ’ to theoretical constructs, 3) validity problems arising from the stability of the constructs over time and 4) counter-intuitive validity issues deriving from the data being near-complete records of communities’ activities. The paper concludes with recommendations for researchers and reviewers designed to increase the rigor and relevance of SNA research in Information Systems.

PROGRAM ANNOUNCEMENT/SOLICITATION NO./CLOSING DATE/if not in response to a program announcement/solicitation enter NSF 07-140

issues in the use of social network analysis with digital trace data

Online production systems represent a new and innovative approach for producing information goods. However, the success of such endeavors depends on a careful interrelationship between their social and technical dimensions. In this paper, we explore how various aspects of those dimensions impact the success of online production systems. We collected data from the open source community GNOME and we used the inclusion of a product into an official release as indicator for the success of a project. Our analyses revealed that structural characteristics of the individual project’s communication and task dependency (coordination needs) networks, the position of individuals in the overall ecosystem communication network as well as the technical structure of the product, are all significantly associated with project success. Our novel results represent an important step in understanding the success drivers of online production systems as well as a starting point for reshaping traditional models for producing information goods typically used in corporate settings.

Research Practice There is an exciting natural match between social network analysis methods and the growth of data sources produced by social interactions via information technologies, from online communities to corporate information systems. Information Systems researchers have not been slow to embrace this combination of method and data. Such systems increasingly provide “digital trace data ” that provide new research opportunities. Yet digital trace data are substantively different from the survey and interview data for which network analysis measures and interpretations were originally developed. This paper examines 10 validity issues associated with the combination of digital trace data and social network analysis methods, with examples from the IS literature, to provide recommendations for improving the validity of future research.

dynamics of free and open source team communications

Validity issues in the use of social network analysis with digital trace data There is an exciting natural match between social network analysis methods and the growth of data sources produced by social interactions via information technologies, from online communities to corporate information systems. Information Systems researchers have not been slow to embrace this combination of method and data. Such systems increasingly provide “digital trace data ” that provide new research opportunities. Yet digital trace data are substantively different from the survey and interview data for which network analysis measures and interpretations were originally developed. This paper examines ten validity issues associated with the combination of data digital trace data and social network analysis methods, with examples from the IS literature, to provide recommendations for improving the validity of research using this combination. 2 2.