A Controlled Experiment in Age and Gender Bias When Reading Technical Articles in Software Engineering

Abstract

Online platforms and communities are a critical part of modern software engineering, yet are often affected by human biases. While previous studies investigated human biases and their potential harms against the efficiency and fairness of online communities, they have mainly focused on the open source and Q & A platforms, such as GitHub and Stack Overflow , but overlooked the audience-focused online platforms for delivering programming and SE-related technical articles, where millions of software engineering practitioners share, seek for, and learn from high-quality software engineering articles (i.e., technical articles for SE). Furthermore, most of the previous work has revealed gender and race bias, but we have little knowledge about the effect of age on software engineering practice. In this paper, we propose to investigate the effect of authors’ demographic information (gender and age) on the evaluation of technical articles on software engineering and potential behavioral differences among participants. We conducted a survey-based and controlled human study and collected responses from 540 participants to investigate developers’ evaluation of technical articles for software engineering. By controlling the gender and age of the author profiles of technical articles for SE, we found that raters tend to have more positive content depth evaluations for younger male authors when compared to older male authors and that male participants conduct technical article evaluations faster than female participants, consistent with prior study findings. Surprisingly, different from other software engineering evaluation activities (e.g., code review, pull request, etc.), we did not find a significant difference in the genders of authors on the evaluation outcome of technical articles in SE.