Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance
This work addresses the need for more robust and generalizable results in software engineering research by demonstrating how mixed-methods replication can improve prior qualitative studies, though it is incremental in applying existing techniques to a specific domain.
The authors tackled the problem of generalizing qualitative findings on GitHub pull request acceptance by replicating and enhancing a prior study with crowdsourcing and data mining on a larger dataset of 170 pull requests, resulting in a predictor with significantly higher accuracy (F1=90% vs 68%).
Crowdsourcing and data mining can be used to effectively reduce the effort associated with the partial replication and enhancement of qualitative studies. For example, in a primary study, other researchers explored factors influencing the fate of GitHub pull requests using an extensive qualitative analysis of 20 pull requests. Guided by their findings, we mapped some of their qualitative insights onto quantitative questions. To determine how well their findings generalize, we collected much more data (170 additional pull requests from 142 GitHub projects). Using crowdsourcing, that data was augmented with subjective qualitative human opinions about how pull requests extended the original issue. The crowd's answers were then combined with quantitative features and, using data mining, used to build a predictor for whether code would be merged. That predictor was far more accurate that one built from the primary study's qualitative factors (F1=90 vs 68\%), illustrating the value of a mixed-methods approach and replication to improve prior results. To test the generality of this approach, the next step in future work is to conduct other studies that extend qualitative studies with crowdsourcing and data mining.