Improving Evidence-Based Tech Hiring with GitHub-Supported Resume Matching

Current hiring practices use technical & soft skills proxy keyword based resume matching via Automated Resume Parsers (ARPs). However, this process fails to extract the actual abilities of candidates, such as the quality of their written code, raising concerns regarding the effectiveness of current approaches. Thus, novel evidences accurately depicting candidates' skills are necessary to inform hiring decisions. We posit GitHub-supported resume matching as a solution, mining data from candidates' open source projects to provide evidence for their technical skills. We conducted a preliminary survey <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(n=48)$</tex> to gain insights from candidates and recruiters on proxies from GitHub projects indicative of technical abilities. We found both groups preferred metrics regarding code quality and used technologies, and there was overwhelming willingness to incorporate this analysis in resume matching tasks. Based on these insights, we designed GitMeter - a tool to capture technical abilities (i.e., code quality) and soft skills of candidates by mining public GitHub repositories. GitMeter uses a novel heuristic-based approach to find the most accurate code quality approximation for candidate-written code (core code), minimizing the time and computational overhead. Finally, we evaluate effectiveness and potential impact of GitMeter through a user study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(n=20)$</tex> with developers and recruiters. Our findings provide implications for future tools and methods aiming to promote evidence-based hiring in software engineering (SE) contexts.