Rating prediction for software developers by integrating OSS community and crowd sourcing

Abstract

Success of software developping project depend on skills of developers in the teams, however, predicting such skills is not a obvious problem. In crowd sourcing services, such level of the skills is rated by the users. This paper aims to predict the rating by integrating open source software (OSS) communities and crowd soursing services. We show that the problem is reduced into the feature construction problem from OSS communities and proposes the s-index, which abstract the level of skills of the developers based on the developed projects. Specifically, we inetgrate oDesk (a crowd sourcing service) and GitHub (an OSS community), and construct prediction model by using the ratings from oDesk as a training data. The experimental result shows that our method outperforms the models without s-index for the aspect of nDCG.