Predicting API Expertise: A Cross-Community Replication Using Zipf's Law

Experience and knowledge are essential and adequate conditions to assess developer expertise. In practice, while various mechanisms are used for assessing developer expertise, research has shown that existing mechanisms may not support the holistic evaluation of developer expertise or contain the complexity behind the development of that expertise (e.g., experience with individual APIs and programming concepts). Prior research has shown that developer contributions on platforms like GitHub and StackOverflow can provide personal insights into broader developer expertise. This paper explores the potential for providing more granular assessments of developer expertise based on their interactions in these online programming communities by empirically analyzing developer contributions on StackOverflow and GitHub related to Python APIs. We found that techniques from prior work scale to support the automated ranking of developers based on their experience with a given Python API. Our findings suggest that we can use syntax patterns in written language and code to detect and rank developers' low-level technical expertise accurately.