Understanding metric-based detectable smells in Python software: A comparative study

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. Due to the great differences between Python...

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Vydané v:Information and software technology Ročník 94; s. 14 - 29
Hlavní autori: Chen, Zhifei, Chen, Lin, Ma, Wanwangying, Zhou, Xiaoyu, Zhou, Yuming, Xu, Baowen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.02.2018
Predmet:
Code smell
Detection strategy
Python
Software maintainability
Code smell
Detection strategy
Software maintainability
Python
ISSN:0950-5849, 1873-6025
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Popis
Shrnutí: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. 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. 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. 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. 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.
ISSN:0950-5849
1873-6025
DOI:10.1016/j.infsof.2017.09.011