分享企业软件测试中应用机器学习的全流程经验，可复用。
Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
- 按标准流程收集、清洗、工程化数据并划分训练测试集。
- 通过模型训练与评估，实现可复现的ML应用结果。
- 适合非专家团队在软件测试中落地机器学习。
本文记录了一支团队在软件测试中开展机器学习的实践历程。该过程遵循类似CRISP-DM的标准化工作流：收集数据、清洗数据、进行特征工程、将数据划分为训练集与测试集、选择机器学习模型、训练模型、测试并评估模型性能。通过这套流程，任何项目团队均可有效应用机器学习技术解决实际问题。
原文摘要 · Abstract (English)
This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project: gather the data; clean the data; perform feature engineering on the data; splitting the data into two sets, one for training and one for testing; choosing a machine learning model; training the model; testing the model and evaluating the model performance. By following this workflow, anyone can effectively apply ML to any project that they are doing.
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