Recommending Programming Languages by Identifying Skill Gaps Using Analysis of Experts. A Study of Stack Overflow

The increasing variety of programming languages available to computer programmers has led to the discussion of what language(s) should be learned. A key point in the choice of a programming language is the availability of support from experienced programmers. In this paper, we explore the use of graph theory in recommending programming languages to novice and expert programmers in a question and answer collaborative learning environment, Stack Overflow. Using social network analysis techniques, we investigate the relationship between experts (using an expertise graph) in different programming languages to identify what languages can be recommended to novice and experienced programmers. In addition, we explore the use of the expertise graph in inferring the importance of a programming language to the community. Our results suggest that programming languages can be recommended within organizational borders and programming domains. In addition, a high number of experts in a programming language does not always mean that the language is popular. Furthermore, disconnected nodes in the expertise graph suggest that experts in some programming languages are primarily on Stack Overflow to support that language only and do not contribute to questions or answers in other languages. Finally, developers are comfortable with mastering a single, general purpose language. The results of our study can help educators and stakeholders in computer education to understand what programming languages can be suggested to students and what languages can be taught and learned together.