A Generic Framework for Concept-Based Exploration of Semi-Structured Software Engineering Data

Gillian J. Greene
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引用次数: 6

Abstract

Software engineering meta-data (SE data), such as revision control data, Github project data or test reports, is typically semi-structured, it comprises a mixture of formatted and free-text fields and is often self-describing. Semi-structured SE data cannot be queried in a SQL-like manner because of its lack of structure. Consequently, there are a variety of customized tools built to analyze specific datasets but these do not generalize. We propose to develop a generic framework for exploration and querying of semi-structured SE data. Our approach investigates the use of a formal concept lattice as a universal data structure and a tag cloud as an intuitive interface to support data exploration.
半结构化软件工程数据基于概念探索的通用框架
软件工程元数据(SE数据),例如修订控制数据、Github项目数据或测试报告,通常是半结构化的,它包含格式化和自由文本字段的混合,并且通常是自描述的。半结构化SE数据不能以类似sql的方式查询,因为它缺乏结构。因此,有各种定制的工具来分析特定的数据集,但这些工具并不能泛化。我们建议开发一个通用的框架来探索和查询半结构化的SE数据。我们的方法研究了形式化概念格作为通用数据结构和标签云作为支持数据探索的直观界面的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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