Implicit gender bias in software development is a well-documented issue, such
as the association of technical roles with men. To address this bias, it is
important to understand it in more detail. This study uses data mining
techniques to investigate the extent to which 56 tasks related to software
development, such as assigning GitHub issues and testing, are affected by
implicit gender bias embedded in large language models. We systematically
translated each task from English into a genderless language and back, and
investigated the pronouns associated with each task. Based on translating each
task 100 times in different permutations, we identify a significant disparity
in the gendered pronoun associations with different tasks. Specifically,
requirements elicitation was associated with the pronoun "he" in only 6% of
cases, while testing was associated with "he" in 100% of cases. Additionally,
tasks related to helping others had a 91% association with "he" while the same
association for tasks related to asking coworkers was only 52%. These findings
reveal a clear pattern of gender bias related to software development tasks and
have important implications for addressing this issue both in the training of
large language models and in broader society.

本研究使用数据挖掘技术，调查了与软件开发相关的 56 项任务，发现不同任务中含有性别代词的比例差异显著，其中测试与帮助他人的任务中含有 “他” 这一性别代词比例显著较高，这些发现揭示了与软件开发任务相关的性别偏见模式。