research-article

House of cards: code smells in open-source C# repositories

Authors:

Marios Fragkoulis,

Diomidis SpinellisAuthors Info & Claims

ESEM '17: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Pages 424 - 429

https://doi.org/10.1109/ESEM.2017.57

Published: 09 November 2017 Publication History

Abstract

Background: Code smells are indicators of quality problems that make a software hard to maintain and evolve. Given the importance of smells in the source code's maintainability, many studies have explored the characteristics of smells and analyzed their effects on the software's quality.

Aim: We aim to investigate fundamental characteristics of code smells through an empirical study on frequently occurring smells that examines inter-category and intra-category correlation between design and implementation smells.

Method: The study mines 19 design smells and 11 implementation smells in 1988 C# repositories containing more than 49 million lines of code. The mined data are statistically analyzed using methods such as Spearman's correlation and presented through hexbin and scatter plots.

Results: We find that unutilized abstraction and magic number smells are the most frequently occurring smells in C# code. Our results also show that implementation and design smells exhibit strong inter-category correlation. The results of co-occurrence analysis imply that whenever unutilized abstraction or magic number smells are found, it is very likely to find other smells from the same smell category in the project.

Conclusions: Our experiment shows high average smell density (14.7 and 55.8 for design and implementation smells respectively) for open source C# programs. Such high smell densities turn a software system into a house of cards reflecting the fragility introduced in the system. Our study advocates greater awareness of smells and the adoption of regular refactoring within the developer community to avoid turning software into a house of cards.

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Cited By

Sharma TSpinellis DConstantinou EBacchelli A(2024)Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0Proceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644881(284-288)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644881
Yu DYang QChen XChen JWang SXu Y(2024)Actionable code smell identification with fusion learning of metrics and semanticsScience of Computer Programming10.1016/j.scico.2024.103110236:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103110
Mumtaz HSingh PBlincoe K(2022)Analyzing the Relationship between Community and Design Smells in Open-Source Software Projects: An Empirical StudyProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546249(23-33)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546249
Show More Cited By

House of cards: code smells in open-source C# repositories
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management

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Information & Contributors

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Published In

cover image ACM Conferences

ESEM '17: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

November 2017

481 pages

ISBN:9781509040391

General Chair:
Ayse Bener
Ryerson University, Canada
,
Program Chairs:
Burak Turhan
Brunel University, UK
,
Stefan Biffl
Technische Universität Wien, Austria

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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IEEE Press

Publication History

Published: 09 November 2017

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ESEM '17

Sponsor:

SIGSOFT
IEEE-CS\DATC

ESEM '17: ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

November 9 - 10, 2017

Ontario, Markham, Canada

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Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

Sharma TSpinellis DConstantinou EBacchelli A(2024)Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0Proceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644881(284-288)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644881
Yu DYang QChen XChen JWang SXu Y(2024)Actionable code smell identification with fusion learning of metrics and semanticsScience of Computer Programming10.1016/j.scico.2024.103110236:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103110
Mumtaz HSingh PBlincoe K(2022)Analyzing the Relationship between Community and Design Smells in Open-Source Software Projects: An Empirical StudyProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546249(23-33)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546249
Firouzi ESami AKhomh FUddin G(2020)On the use of C# Unsafe Code ContextProceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3382494.3422165(1-6)Online publication date: 5-Oct-2020
https://dl.acm.org/doi/10.1145/3382494.3422165
Sharma TAvgeriou PSchmid K(2019)How deep is the mudProceedings of the Second International Conference on Technical Debt10.1109/TechDebt.2019.00018(59-60)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/TechDebt.2019.00018

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