
Abstract Context:Modern software development demands high levels of technical specialization. These conditions make IT companies focus on creating cross-functional teams, such as frontend, backend, and mobile developers. In this context, the success of software projects is highly influenced by the expertise of these teams in each field. Objective:In this paper, we investigate machine-learning based approaches to automatically identify the technical roles of open source developers. Method:For this, we first build a ground truth with 2284 developers labeled in six different roles: backend, frontend, full-stack, mobile, devops, and data science. Then, we build three different machine-learning models used to identify these roles. Results:These models presented competitive results for precision (0.88) and AUC (0.89) when identifying all six roles. Moreover, our results show that programming-languages are the most relevant features to predict the investigated roles. Conclusion:The approach proposed in this paper can assist companies during their hiring process, such as by recommending developers with the expertise required by job positions.
| citations The total number of citations received by all articles of the researcher of interest. | 35 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
