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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. Here, we outline practical lessons gained from our spidering, parsing and analysis of SourceForge data. SourceForge can be practically difficult: projects are defunct, data from earlier systems has been dumped in and crucial data is hosted outside SourceForge, dirtying the retrieved data. These practical issues play directly into analysis: decisions made in screening projects can reduce the range of variables, skewing data and biasing correlations. SourceForge is theoretically perilous: because it provides easily accessible data items for each project, tempting researchers to fit their theories to these limited data. Worse, few are plausible dependent variables. Studies are thus likely to test the same hypotheses even if they start from different theoretical bases. To avoid these problems, analyses of SourceForge projects should go beyond project level variables and carefully consider which variables are used for screening projects and which for testing hypotheses.
James Howison (Thu,) studied this question.
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