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Exploranative Code Quality Documents.

Haris Mumtaz, Shahid Latif, Fabian Beck

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    Summary
    This summary is machine-generated.

    This study introduces a novel method for generating interactive, data-driven documents to assess software code quality. The approach uses natural language generation and visualizations to create explanatory reports for maintainable software development.

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    Area of Science:

    • Software Engineering
    • Visual Analytics
    • Human-Computer Interaction

    Background:

    • Maintaining high code quality is crucial for efficient and sustainable software development.
    • Existing methods for code quality assessment often lack interactivity and comprehensive explanations.
    • There is a need for tools that can effectively communicate complex code quality metrics to developers.

    Purpose of the Study:

    • To present a novel approach for generating exploranative (explanatory and exploratory) data-driven documents for code quality assessment.
    • To enable interactive exploration of software quality metrics through integrated text and visualizations.
    • To enhance developer understanding of code quality aspects via embedded background knowledge.

    Main Methods:

    • Utilizing a template-based natural language generation (NLG) technique to create textual explanations of code quality.
    • Integrating diverse visualizations, such as parallel coordinates plots and scatterplots, for data exploration.
    • Developing an interaction model for consistent linking between textual explanations and visual representations.
    • Employing a design study process involving software engineering and visual analytics experts.

    Main Results:

    • Successful generation of interactive documents that explain code quality based on software metrics.
    • Effective integration of visualizations that support data exploration and understanding.
    • Demonstrated ability to link textual explanations with visualizations for a cohesive user experience.
    • Development of a framework that educates users on code quality aspects.

    Conclusions:

    • The proposed approach offers a novel way to report and explore code quality interactively.
    • The method effectively combines natural language generation and visualization for enhanced understanding.
    • The concept shows potential for generalization to other domains involving multivariate data analysis.
    • Lessons learned from the design study can inform broader applications of data-driven reporting.