Vitor Polezi Pesce de Campos, José Maria N. David, Victor Ströele, R. Braga
{"title":"Supporting the recruitment of software development experts: aligning technical knowledge to an industry domain","authors":"Vitor Polezi Pesce de Campos, José Maria N. David, Victor Ströele, R. Braga","doi":"10.5753/sbsc.2023.229069","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":413784,"journal":{"name":"Anais do XVIII Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2023)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do XVIII Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2023)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/sbsc.2023.229069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.