One the one hand, as a GitHub profile is becoming an essential part of a developer’s resume it becomes increasingly important to enable HR departments to extract someone’s expertise, through automated analysis of his/her contribution to open-source projects. On the other hand, having clear insights on the technologies used in a project can be very beneficial for resource allocation and project maintainability planning. In the literature, one can identify various approaches for identifying expertise on programming languages, based on the projects that developer contributed to. In this paper, we move one step further and introduce an approach (accompanied by a tool) to identify low-level expertise on particular software frameworks and technologies apart, relying solely on GitHub data, using the GitHub API and Natural Language Processing (NLP)—using the Microsoft Language Understanding Intelligent Service (LUIS). In particular, we developed an NLP model in LUIS for named-entity recognition for three (3) .NET technologies and two (2) front-end frameworks. Our analysis is based upon specific commit contents, in terms of the exact code chunks, which the committer added or changed. We evaluate the precision, recall and f-measure for the derived technologies/frameworks, by conducting a batch test in LUIS and report the results. The proposed approach is demonstrated through a fully functional web application named RepoSkillMiner.

Tool Links: Video, Code Repo, Application, Validation Dataset

CCS CONCEPTS • Software and its engineering → Software creation and manage-ment -> Software post-development issues;

KEYWORDS Expertise; Frameworks; GitHub; Natural Language Processing; Soft-ware Project Management;