Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.

中文翻译：

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解释性代码质量文档。

良好的代码质量是有效开发可维护软件的先决条件。在本文中，我们提出了一种新颖的方法来生成解释性（解释性和探索性）数据驱动的文档，这些文档在交互式探索性环境中报告代码质量。我们采用基于模板的自然语言生成方法来创建有关代码质量的文本说明，具体取决于来自软件指标的数据。交互式文档通过不同类型的可视化来丰富，包括平行坐标图和用于数据探索的散点图以及嵌入文本的图形。我们设计了一个交互模型，使用户可以通过文本和可视化之间的一致链接来探索代码质量；通过集成的解释性文本，可以为用户提供有关代码质量方面的背景知识。我们的交互式文档方法是在包括软件工程和视觉分析专家在内的设计研究过程中开发的。尽管该解决方案特定于软件工程场景，但我们讨论了如何将该概念推广到多元数据并报告更广泛范围内的经验教训。