Recommending relevant projects via user behaviour: an exploratory study on github
Lingxiao Zhang, Yanzhen Zou, Bing Xie, Zixiao Zhu · 2014
Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd-based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many fields have been using user behavior data to discover user preferences and interests to achieve various purposes. In the field of software engineering however, there has been few studies in mining large amount of user behavior data. Our goal is to design an approach based on user behavior data, to recommend relevant open source projects to developers, which can be helpful in activities like searching for the right open source solutions to quickly build prototypes. In this paper, we explore the possibilities of such a method by conducting a set of experiments on selected data sets from Github. We find it a promising direction in mining projects' relevance from user behavior data. Our study also obtain some important issues that is worth considering in this method.