Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICPC.2013.6613836 | Program Comprehension |
Keywords | Field | DocType |
C++ language,Java,learning (artificial intelligence),program compilers,program diagnostics,reverse engineering,software metrics,software quality,system documentation,C/C++ programs,Java programs,code comment assessment,comment categories,machine learning,program understanding,software metrics,software quality analysis,software systems,source code comment quality analysis,system documentation | Static program analysis,Systems engineering,Software engineering,Source code,Computer science,Internal documentation,Software quality control,KPI-driven code analysis,Software construction,Software quality,Code review | Conference |
ISSN | Citations | PageRank |
1063-6897 | 45 | 1.86 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniela Steidl | 1 | 103 | 5.59 |
| Benjamin Hummel | 2 | 660 | 29.51 |
| Elmar Jürgens | 3 | 45 | 1.86 |