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.

翻译：软件开发中隐性性别偏见是一个有充分记录的问题，例如技术角色常与男性关联。为应对这一偏见，深入理解其细节至关重要。本研究采用数据挖掘技术，探究与软件开发相关的56项任务（如分配GitHub议题、测试等）受大型语言模型中隐性性别偏见影响的程度。我们系统地将每项任务从英语翻译为无性别语言再译回原语言，并调查各任务关联的代词。基于每项任务以不同排列翻译100次的结果，我们发现不同任务与性别代词的关联存在显著差异。具体而言，需求获取任务中仅6%的案例关联代词"he"，而测试任务中100%的案例关联"he"。此外，帮助他人的任务与"he"的关联度为91%，而询问同事的任务关联度仅为52%。这些发现揭示了与软件开发任务相关的清晰性别偏见模式，对在大语言模型训练及更广泛社会中解决该问题具有重要启示。