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.

背景：现代软件开发需要高水平的技术专业化。这些条件使IT公司专注于创建跨职能团队，例如前端，后端和移动开发人员。在这种情况下，软件项目的成功很大程度上取决于这些团队在各个领域的专业知识。

目的：在本文中，我们研究了基于机器学习的方法，以自动识别开源开发人员的技术角色。

方法：为此，我们首先用2284个开发人员构建了一个基本事实，这些开发人员被标记为六个不同的角色：后端，前端，全栈，移动，devop和数据科学。然后，我们建立了三种不同的机器学习模型来识别这些角色。

结果：当确定所有六个角色时，这些模型在精度（0.88）和AUC（0.89）方面均具有竞争性结果。此外，我们的结果表明，编程语言是预测所研究角色的最相关功能。

结论：本文提出的方法可以在公司的招聘过程中提供帮助，例如通过推荐具有职位所需专业知识的开发人员。