# Exploranative Code Quality Documents

## TL;DR

This paper introduces an interactive, data-driven document system that explains and explores code quality using natural language explanations, visualizations, and user interactions to enhance understanding and maintainability.

## Contribution

It presents a novel template-based natural language generation approach combined with interactive visualizations for exploring code quality data.

## Key findings

- Effective integration of text and visualizations for code quality analysis
- Design of an interaction model linking explanations with data exploration
- Lessons learned on generalizing the approach to multivariate data

## Abstract

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.