Mining Experts from Source Code Analysis: An Empirical Evaluation

Authors

  • Johnatan Alves Oliveira Federal University of Minas Gerais (UFMG)
  • Markos Viggiato University of Alberta
  • Denis Pinheiro Federal University of Minas Gerais (UFMG)
  • Eduardo Figueiredo Federal University of Minas Gerais (UFMG)

DOI:

https://doi.org/10.5753/jserd.2021.548

Keywords:

Library Experts, Software Skills, Expert Identification, Mining Software Repositories

Abstract

Third-party libraries have been widely adopted in modern software projects due to several benefits, such as code reuse and software quality. Software development is increasingly complex and requires specialists with knowledge in several technologies, such as the nowadays libraries. Such complexity turns it extremely challenging to deliver quality software, given the time pressure. For this purpose, it is necessary to identify and hire qualified developers, to obtain a good team, both in open source and proprietary systems. For these reasons, enterprise and open source projects try to build teams composed of highly skilled developers in specific libraries. Developers with expertise in specific libraries may reduce the time spent on software development tasks and improve the quality of the final product. However, their identification may not be trivial. In this paper, we first argue that source code activities can be used to identify the hard skills of software developers, such as library expertise. We then evaluate a mining-based strategy to identify library experts. To achieve our goal, we selected the 9 most popular Java libraries and evaluated the skills of more than 1.5 million developers in these libraries by analyzing their commits in 16,703 Java projects on GitHub. We validated the results by applying a survey with 137 library expert candidates and observed, on average, 88% of precision for the applied strategy.

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Published

2021-02-08

How to Cite

Oliveira, J. A., Viggiato, M., Pinheiro, D., & Figueiredo, E. (2021). Mining Experts from Source Code Analysis: An Empirical Evaluation. Journal of Software Engineering Research and Development, 9(1), 1:1 – 1:16. https://doi.org/10.5753/jserd.2021.548

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Section

Research Article

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