Quality analysis of source code comments

Daniela Steidl, B. Hummel, Elmar Jürgens
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引用次数: 169

Abstract

A significant amount of source code in software systems consists of comments, i. e., parts of the code which are ignored by the compiler. Comments in code represent a main source for system documentation and are hence key for source code understanding with respect to development and maintenance. Although many software developers consider comments to be crucial for program understanding, existing approaches for software quality analysis ignore system commenting or make only quantitative claims. Hence, current quality analyzes do not take a significant part of the software into account. In this work, we present a first detailed approach for quality analysis and assessment of code comments. The approach provides a model for comment quality which is based on different comment categories. To categorize comments, we use machine learning on Java and C/C++ programs. The model comprises different quality aspects: by providing metrics tailored to suit specific categories, we show how quality aspects of the model can be assessed. The validity of the metrics is evaluated with a survey among 16 experienced software developers, a case study demonstrates the relevance of the metrics in practice.
源代码注释的质量分析
在软件系统中,相当数量的源代码由注释组成,即编译器忽略的代码部分。代码中的注释代表了系统文档的主要来源,因此对于开发和维护方面的源代码理解至关重要。尽管许多软件开发人员认为注释对于程序理解是至关重要的,但是现有的软件质量分析方法忽略了系统注释,或者只做定量的声明。因此,当前的质量分析没有考虑到软件的重要部分。在这项工作中,我们提出了代码注释的质量分析和评估的第一个详细方法。该方法提供了一个基于不同评论类别的评论质量模型。为了对注释进行分类,我们在Java和C/ c++程序中使用机器学习。模型包含不同的质量方面:通过提供适合特定类别的度量,我们展示了如何评估模型的质量方面。通过对16位有经验的软件开发人员的调查来评估度量标准的有效性,一个案例研究证明了度量标准在实践中的相关性。
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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