research-article

Understanding metric-based detectable smells in Python software

Authors:

Wanwangying Ma,

Baowen XuAuthors Info & Claims

Information and Software Technology, Volume 94, Issue C

Pages 14 - 29

Published: 01 February 2018 Publication History

Abstract

ContextCode 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. ObjectiveDue 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. MethodIn 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. ResultsThe 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. ConclusionThese 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.

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Cited By

Zhang BLiang PFeng QFu YLi ZFilkov VRay BZhou M(2024)Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using CopilotProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695290(2230-2234)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695290
Jebnoun HBen Braiek HRahman MKhomh F(2020)The Scent of Deep Learning CodeProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387479(420-430)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1145/3379597.3387479
AbuHassan AAlshayeb MGhouti L(2020)Software smell detection techniquesJournal of Software: Evolution and Process10.1002/smr.232033:3Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1002/smr.2320
Show More Cited By

Understanding metric-based detectable smells in Python software
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management

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cover image Information and Software Technology

Information and Software Technology Volume 94, Issue C

February 2018

262 pages

ISSN:0950-5849

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Copyright © Elsevier B.V.

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Butterworth-Heinemann

United States

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Published: 01 February 2018

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Cited By

Zhang BLiang PFeng QFu YLi ZFilkov VRay BZhou M(2024)Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using CopilotProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695290(2230-2234)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695290
Jebnoun HBen Braiek HRahman MKhomh F(2020)The Scent of Deep Learning CodeProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387479(420-430)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1145/3379597.3387479
AbuHassan AAlshayeb MGhouti L(2020)Software smell detection techniquesJournal of Software: Evolution and Process10.1002/smr.232033:3Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1002/smr.2320
Lukasczyk SZimmermann TLawall JMarinov D(2019)Generating tests to analyse dynamically-typed programsProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00146(1226-1229)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00146

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