Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in ML projects.

机器学习 (ML) 技术和算法已成功并广泛应用于各个领域，包括软件工程任务。与其他软件项目一样，错误在 ML 项目和库中也很常见。为了更深入地了解 ML 项目中与 bug 修复相关的特性，我们通过手动检查 bug 类别、修复模式、修复规模、修复持续时间和维护类型，对来自 5 个 ML 项目的 939 个 bug 进行了实证研究。结果表明：（1）机器学习程序中通常存在七种类型的错误；(2) 12 种修复模式通常用于修复 ML 程序中的错误；(3) 68.80%的补丁属于micro-scale-fix和small-scale-fix；(4) 1个月内修复ML程序66.77%的bug；(5) 45。从软件维护的角度来看，90% 的错误修复属于纠正活动。此外，我们进行问卷调查并将其发送给 ML 项目的开发人员或用户，以验证我们的实证研究结果。我们的实证研究结果与开发者的反馈基本一致。实证研究的结果为开发人员和用户提供有用的指导和见解，以有效检测和修复 ML 项目中的错误。