一句话：通过医学影像与眼动追踪的对照实验，揭示代码评审中性别差异以及基于作者身份的偏见。
代码评审是软件质量保证的关键环节，但易受人类偏见影响，已有研究对评审者偏见的程度尚无共识。本文开展对照实验，结合医学影像与眼动追踪，考察代码评审中人类与机器（如自动程序修复工具）之间的性别差异及神经相关因素。结果发现，男性和女性进行代码评审的方式存在可测量的差异，行为、眼动和医学影像数据均支持这一点；在控制代码质量后，评审仍会因代码表面作者身份而产生偏见。该研究深化了对认知偏见与评审过程关系的理解，并为后续培训和工具设计以减少偏见提供依据。
Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code,but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias.
DOI 原文 ·
@inproceedings{paperbot5827,
title = {Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines},
author = {Yu Huang and Kevin Leach and Zohreh Sharafi and Nicholas McKay and Tyler Santander and Westley Weimer},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409681}
}