论文标题
公平测试：对趋势的全面调查和分析
Fairness Testing: A Comprehensive Survey and Analysis of Trends
论文作者
论文摘要
机器学习（ML）软件的不公平行为已吸引了软件工程师的关注和关注。为了解决这个问题，广泛的研究致力于对ML软件进行公平性测试，本文对该领域的现有研究进行了全面的调查。我们根据测试工作流（即如何测试）和测试组件（即测试方法）收集100篇论文并根据测试工作流程（即如何测试）进行组织。此外，我们分析了公平测试领域的研究重点，趋势和有希望的方向。我们还确定了公平测试的广泛补充数据集和开源工具。
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing.
