Supporting the recruitment of software development experts: aligning technical knowledge to an industry domain
Resumo
Finding experts that meet specific technical skills, combined with expertise in an industry domain, is essential in software development environments. However, this may be a complex task once different information about software developers is scattered among diverse databases. This work aims to detect experts and assemble a list of recommended experts regarding technologies and industry domains of interest. Data from LinkedIn, GitHub, and Topcoder platforms were used to achieve this goal. Our approach matches data using semantic and syntactic techniques and infers non-obvious information through an ontology. The information regarding the recommended software developers has the potential to support decision-makers and recruiters.
Palavras-chave:
expert recommendation, recommendation system, semantic model, ontology, global software development
Referências
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Alarfaj, F., Kruschwitz, U., Hunter, D., and Fox, C. (2012). Finding the right supervisor: Expert-finding in a university domain. pages 1–6.
Beecham, S., Carroll, N., and Noll, J. (2012). A decision support system for global team management: Expert evaluation remidi.
Buneman, P., Khanna, S., and Wang-Chiew, T. (2001). Why and where: A characterization of data provenance. In International conference on database theory, pages 316–330. Springer.
Cifariello, P., Ferragina, P., and Ponza, M. (2019). Wiser: A semantic approach for expert finding in academia based on entity linking. Information Systems, 82:1–16.
Frey, C. and Osborne, M. (2015). Technology at Work: The Future of Innovation and Employment.
Ghaisas, S. (U.S. Patent 9262126B2, 2010). Recommendation system for agile software development.
Guarino, N., Oberle, D., and Staab, S. (2009). What Is an Ontology?, pages 1–17. Springer Berlin Heidelberg, Berlin, Heidelberg.
Herre, H. (2010). General Formal Ontology (GFO): A Foundational Ontology for Conceptual Modelling, pages 297–345. Springer Netherlands, Dordrecht.
Hoehndorf, R., Gkoutos, G. V., and Schofield, P. N. (2016). Datamining with ontologies. Methods Mol Biol, 1415:385–397.
Hyrynsalmi, S. M., Rantanen, M. M., and Hyrynsalmi, S. (2021). The war for talent in software business - how are finnish software companies perceiving and coping with the labor shortage? In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pages 1–10.
Knoke, D. and Yang, S. (2008). Social Network Analysis, volume 154.
Lin, S., Hong, W., Wang, D., and Li, T. (2017). A survey on expert finding techniques. Journal of Intelligent Information Systems, 49(2):255–279.
Lopes, T., Ströele, V., Braga, R., David, J. M. N., and Bauer, M. (2021). Identifying and recommending experts using a syntactic-semantic analysis approach. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 739–744.
Martínez-García, J. R., Castillo-Barrera, F.-E., Palacio, R. R., Borrego, G., and Cuevas-Tello, J. C. (2020). Ontology for knowledge condensation to support expertise location in the code phase during software development process. IET Software, 14(3):234–241.
Matturro, G., Raschetti, F., and Fontán, C. (2019). A systematic mapping study on soft skills in software engineering. Journal of Universal Computer Science, 25:16–41.
Miloslavskaya, N. and Tolstoy, A. (2016). Big data, fast data and data lake concepts. Procedia Computer Science, 88:300–305.
Neira, A., Steinmacher, I., and Wiese, I. (2018). Characterizing the hyperspecialists in the context of crowdsourcing software development. Journal of the Brazilian Computer Society, 24:17.
Özsu, M. T. and Valduriez, P. (1999). Principles of distributed database systems, volume 2. Springer.
Pereira, T. A. B., dos Santos, V. S., Ribeiro, B. L., and Elias, G. (2010). A recommendation framework for allocating global software teams in software product line projects. In Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering, RSSE ’10, page 36–40, New York, NY, USA. Association for Computing Machinery.
Pourheidari, V., Mollashahi, E. S., Vassileva, J., and Deters, R. (2018). Recommender system based on extracted data from different social media. a study of twitter and linkedin. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pages 215–222.
Ricci, F., Rokach, L., and Shapira, B. (2022). Recommender Systems: Techniques, Applications, and Challenges, pages 1–35. Springer US, New York, NY.
Schwab, K. (2016). The fourth industrial revolution.
Zhang, X.,Wang, T., Yin, G., Yang, C., Yu, Y., and Wang, H. (2017). Devrec: A developer recommendation system for open source repositories. In Botterweck, G. and Werner, C., editors, Mastering Scale and Complexity in Software Reuse, pages 3–11, Cham. Springer International Publishing.
Alarfaj, F., Kruschwitz, U., Hunter, D., and Fox, C. (2012). Finding the right supervisor: Expert-finding in a university domain. pages 1–6.
Beecham, S., Carroll, N., and Noll, J. (2012). A decision support system for global team management: Expert evaluation remidi.
Buneman, P., Khanna, S., and Wang-Chiew, T. (2001). Why and where: A characterization of data provenance. In International conference on database theory, pages 316–330. Springer.
Cifariello, P., Ferragina, P., and Ponza, M. (2019). Wiser: A semantic approach for expert finding in academia based on entity linking. Information Systems, 82:1–16.
Frey, C. and Osborne, M. (2015). Technology at Work: The Future of Innovation and Employment.
Ghaisas, S. (U.S. Patent 9262126B2, 2010). Recommendation system for agile software development.
Guarino, N., Oberle, D., and Staab, S. (2009). What Is an Ontology?, pages 1–17. Springer Berlin Heidelberg, Berlin, Heidelberg.
Herre, H. (2010). General Formal Ontology (GFO): A Foundational Ontology for Conceptual Modelling, pages 297–345. Springer Netherlands, Dordrecht.
Hoehndorf, R., Gkoutos, G. V., and Schofield, P. N. (2016). Datamining with ontologies. Methods Mol Biol, 1415:385–397.
Hyrynsalmi, S. M., Rantanen, M. M., and Hyrynsalmi, S. (2021). The war for talent in software business - how are finnish software companies perceiving and coping with the labor shortage? In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pages 1–10.
Knoke, D. and Yang, S. (2008). Social Network Analysis, volume 154.
Lin, S., Hong, W., Wang, D., and Li, T. (2017). A survey on expert finding techniques. Journal of Intelligent Information Systems, 49(2):255–279.
Lopes, T., Ströele, V., Braga, R., David, J. M. N., and Bauer, M. (2021). Identifying and recommending experts using a syntactic-semantic analysis approach. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 739–744.
Martínez-García, J. R., Castillo-Barrera, F.-E., Palacio, R. R., Borrego, G., and Cuevas-Tello, J. C. (2020). Ontology for knowledge condensation to support expertise location in the code phase during software development process. IET Software, 14(3):234–241.
Matturro, G., Raschetti, F., and Fontán, C. (2019). A systematic mapping study on soft skills in software engineering. Journal of Universal Computer Science, 25:16–41.
Miloslavskaya, N. and Tolstoy, A. (2016). Big data, fast data and data lake concepts. Procedia Computer Science, 88:300–305.
Neira, A., Steinmacher, I., and Wiese, I. (2018). Characterizing the hyperspecialists in the context of crowdsourcing software development. Journal of the Brazilian Computer Society, 24:17.
Özsu, M. T. and Valduriez, P. (1999). Principles of distributed database systems, volume 2. Springer.
Pereira, T. A. B., dos Santos, V. S., Ribeiro, B. L., and Elias, G. (2010). A recommendation framework for allocating global software teams in software product line projects. In Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering, RSSE ’10, page 36–40, New York, NY, USA. Association for Computing Machinery.
Pourheidari, V., Mollashahi, E. S., Vassileva, J., and Deters, R. (2018). Recommender system based on extracted data from different social media. a study of twitter and linkedin. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pages 215–222.
Ricci, F., Rokach, L., and Shapira, B. (2022). Recommender Systems: Techniques, Applications, and Challenges, pages 1–35. Springer US, New York, NY.
Schwab, K. (2016). The fourth industrial revolution.
Zhang, X.,Wang, T., Yin, G., Yang, C., Yu, Y., and Wang, H. (2017). Devrec: A developer recommendation system for open source repositories. In Botterweck, G. and Werner, C., editors, Mastering Scale and Complexity in Software Reuse, pages 3–11, Cham. Springer International Publishing.
Publicado
22/05/2023
Como Citar
DE CAMPOS, Vitor; DAVID, José Maria N.; STRÖELE, Victor; BRAGA, Regina.
Supporting the recruitment of software development experts: aligning technical knowledge to an industry domain. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 18. , 2023, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 183-192.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2023.229069.
