.png){kind=icon}
返回
Understanding metric-based detectable smells in Python software: A comparative study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.infsof.2017.09.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Context Code smells are supposed to cause potential comprehension and maintenance problems in software development. Although code smells are studied in many languages, e.g. Java and C#, there is a lack of technique or tool support addressing code smells in Python. Objective: Due to the great differences between Python and static languages, the goal of this study is to define and detect code smells in Python programs and to explore the effects of Python smells on software maintainability. Method: In this paper, we introduced ten code smells and established a metric-based detection method with three different filtering strategies to specify Metric thresholds (Experience-Based Strategy, Statistics-Based Strategy, and Tuning Machine Strategy). Then, we performed a Comparative study to investigate how three detection strategies perform in detecting Python smells and how these smells affect software maintainability with different detection strategies. This study utilized a corpus of 106 Python projects with most stars on GitHub. Results: The results showed that: (1) the metric-based detection approach performs well in detecting Python smells and Tuning Machine Strategy achieves the best accuracy; (2) the three detection strategies discover some different smell occurrences, and Long Parameter List and Long Method are more prevalent than other smells; (3) several kinds of code smells are more significantly related to changes or faults in Python modules. Conclusion: These findings reveal the key features of Python smells and also provide a guideline for the choice of detection strategy in detecting and analyzing Python smells.
Keyword:
Python
Code smell
Detection strategy
Software maintainability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.8K
被引数:
7.7K
机构
引用论文
.png){kind=icon}
Dynamic and Automatic Feedback-Based Threshold Adaptation for Code Smell Detection用于代码气味检测的基于动态和自动反馈的阈值自适应
PREAI
分享收藏
A controlled experiment investigation of an object-oriented design heuristic for maintainability
PREAI
分享收藏
查看更多内容