论文标题
编辑AI：混合人类代码模式的作者
Editable AI: Mixed Human-AI Authoring of Code Patterns
论文作者
论文摘要
创作HTML文档的开发人员定义了元素以下模式，这些模式可以建立和反映文档的视觉结构，例如，通过将类应用于每个图像，将所有图像在页脚中以相同的高度使所有图像。为了将这些模式呈现给开发人员，并支持开发人员创作与这些模式一致的开发人员，我们提出了一种混合的人类AI技术来创建代码模式。首先通过决策树从单个HTML文档中学到模式，生成开发人员可以查看和编辑的表示形式。代码模式用于为开发人员自动完成建议，列表示例和违规标志。为了评估我们的技术，我们进行了一项用户研究，其中24位参与者编写，编辑和更正了HTML文档。我们发现我们的技术使开发人员能够更快地编辑和更正文档，并更成功地创建，编辑和更正文档。
Developers authoring HTML documents define elements following patterns which establish and reflect the visual structure of a document, such as making all images in a footer the same height by applying a class to each. To surface these patterns to developers and support developers in authoring consistent with these patterns, we propose a mixed human-AI technique for creating code patterns. Patterns are first learned from individual HTML documents through a decision tree, generating a representation which developers may view and edit. Code patterns are used to offer developers autocomplete suggestions, list examples, and flag violations. To evaluate our technique, we conducted a user study in which 24 participants wrote, edited, and corrected HTML documents. We found that our technique enabled developers to edit and correct documents more quickly and create, edit, and correct documents more successfully.
