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Crowdsourcing and data mining can be used to effectively reduce the effortassociated with the partial replication and enhancement of qualitative studies.  For example, in a primary study, other researchers explored factorsinfluencing the fate of GitHub pull requests using an extensive qualitativeanalysis of 20 pull requests. Guided by their findings, we mapped some of theirqualitative insights onto quantitative questions. To determine how well theirfindings generalize, we collected much more data (170 additional pull requestsfrom 142 GitHub projects). Using crowdsourcing, that data was augmented withsubjective qualitative human opinions about how pull requests extended theoriginal issue. The crowd's answers were then combined with quantitativefeatures and, using data mining, used to build a predictor for whether codewould be merged. That predictor was far more accurate that one built from theprimary study's qualitative factors (F1=90 vs 68\%), illustrating the value ofa mixed-methods approach and replication to improve prior results.  To test the generality of this approach, the next step in future work is toconduct other studies that extend qualitative studies with crowdsourcing anddata mining.

Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance