While systemic workplace bias is well-documented in non-computing fields, its specific impact on software engineers remains poorly understood. This study addresses that gap by applying Social Identity Theory (SIT) to investigate four distinct forms of bias: lack of career development, stereotyped task selection, unwelcoming environments, and identity attacks. Using a vignette-based survey, we quantified the prevalence of these biases, identified the demographics most affected, assessed their consequences, and explored the motivations behind biased actions. Our results show that career development and task selection biases are the most prevalent forms, with over two-thirds of victims experiencing them multiple times. Women were more than three times as likely as men to face career development bias, task selection bias, and an unwelcoming environment. In parallel, individuals from marginalized ethnic backgrounds were disproportionately targeted by identity attacks. Our analysis also confirms that, beyond gender and race, factors such as age, years of experience, organization size, and geographic location are significant predictors of bias victimization.

翻译：尽管系统性职场偏见在非计算领域已有充分记录，但其对软件工程师的具体影响仍鲜为人知。本研究通过应用社会身份理论（SIT）来调查四种不同形式的偏见：职业发展机会缺失、刻板化任务分配、非包容性环境以及身份攻击，从而填补了这一研究空白。我们采用情景式问卷调查，量化了这些偏见的普遍程度，识别了受影响最严重的人口群体，评估了其后果，并探究了偏见行为背后的动机。研究结果表明，职业发展偏见和任务分配偏见是最普遍的两种形式，超过三分之二的受害者曾多次经历此类情况。女性遭遇职业发展偏见、任务分配偏见和非包容性环境的可能性是男性的三倍以上。与此同时，来自边缘化族裔背景的个体遭受身份攻击的比例异常偏高。我们的分析还证实，除性别和种族外，年龄、工作年限、组织规模和地理位置等因素也是预测偏见受害情况的重要指标。