research-article

Building empirical support for automated code smell detection

Authors:

Jan Schumacher,

Carolyn Seaman, and

Michele ShawAuthors Info & Claims

ESEM '10: Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 2010

Article No.: 8, Pages 1 - 10

https://doi.org/10.1145/1852786.1852797

Published: 16 September 2010 Publication History

Abstract

Identifying refactoring opportunities in software systems is an important activity in today's agile development environments. The concept of code smells has been proposed to characterize different types of design shortcomings in code. Additionally, metric-based detection algorithms claim to identify the "smelly" components automatically. This paper presents results for an empirical study performed in a commercial environment. The study investigates the way professional software developers detect god class code smells, then compares these results to automatic classification. The results show that, even though the subjects perceive detecting god classes as an easy task, the agreement for the classification is low. Misplaced methods are a strong driver for letting subjects identify god classes as such. Earlier proposed metric-based detection approaches performed well compared to the human classification. These results lead to the conclusion that an automated metric-based pre-selection decreases the effort spent on manual code inspections. Automatic detection accompanied by a manual review increases the overall confidence in the results of metric-based classifiers.

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Cited By

Tandon SKumar VSingh V(2024)Study of Code Smells: A Review and Research AgendaInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2024.9.3.0259:3(472-498)Online publication date: 1-Jun-2024
https://doi.org/10.33889/IJMEMS.2024.9.3.025
Mourya SSingh PSingh V(2024)An Insight into Code Smell Detection ToolReliability Engineering for Industrial Processes10.1007/978-3-031-55048-5_17(245-273)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-55048-5_17
Slivka JLuburić NProkić SGrujić KKovačević ASladić GVidaković D(2023)Towards a systematic approach to manual annotation of code smellsScience of Computer Programming10.1016/j.scico.2023.102999(102999)Online publication date: Jul-2023
https://doi.org/10.1016/j.scico.2023.102999
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Index Terms

Building empirical support for automated code smell detection
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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Published In

cover image ACM Conferences

ESEM '10: Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 2010

423 pages

ISBN:9781450300391

DOI:10.1145/1852786

General Chair:
Giancarlo Succi
Free University of Bolzano, Italy
,
Program Chairs:
Maurizio Morisio
Politecnico di Torino, Italy
,
Nachi Nagappan
Microsoft Research

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 September 2010

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Division of Computing and Communication Foundations

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ESEM '10

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SIGSOFT

ESEM '10: 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 16 - 17, 2010

Bolzano-Bozen, Italy

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Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

Tandon SKumar VSingh V(2024)Study of Code Smells: A Review and Research AgendaInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2024.9.3.0259:3(472-498)Online publication date: 1-Jun-2024
https://doi.org/10.33889/IJMEMS.2024.9.3.025
Mourya SSingh PSingh V(2024)An Insight into Code Smell Detection ToolReliability Engineering for Industrial Processes10.1007/978-3-031-55048-5_17(245-273)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-55048-5_17
Slivka JLuburić NProkić SGrujić KKovačević ASladić GVidaković D(2023)Towards a systematic approach to manual annotation of code smellsScience of Computer Programming10.1016/j.scico.2023.102999(102999)Online publication date: Jul-2023
https://doi.org/10.1016/j.scico.2023.102999
Santos GSantana AVale GFigueiredo E(2023)Yet Another Model! A Study on Model’s Similarities for Defect and Code SmellsFundamental Approaches to Software Engineering10.1007/978-3-031-30826-0_16(282-305)Online publication date: 22-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30826-0_16
Ichtsis AMittas NAmpatzoglou AChatzigeorgiou AErnst NLenarduzzi VSharma T(2022)Merging smell detectorsProceedings of the International Conference on Technical Debt10.1145/3524843.3528089(61-65)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524843.3528089
Yedida RMenzies TLo DMcIntosh SNovielli N(2022)How to improve deep learning for software analyticsProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3528458(156-166)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1145/3524842.3528458
Li ZChen TYang JShang W(2022)Studying Duplicate Logging Statements and Their Relationships With Code ClonesIEEE Transactions on Software Engineering10.1109/TSE.2021.306091848:7(2476-2494)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSE.2021.3060918
Oliveira DAssunção WGarcia AFonseca BRibeiro M(2022)Developers’ perception matters: machine learning to detect developer-sensitive smellsEmpirical Software Engineering10.1007/s10664-022-10234-227:7Online publication date: 12-Oct-2022
https://dl.acm.org/doi/10.1007/s10664-022-10234-2
Orosz TVági RCsányi GNagy DÜveges IVadász JMegyeri A(2021)Evaluating Human versus Machine Learning Performance in a LegalTech ProblemApplied Sciences10.3390/app1201029712:1(297)Online publication date: 29-Dec-2021
https://doi.org/10.3390/app12010297
Sobrinho EDe Lucia AMaia M(2021)A Systematic Literature Review on Bad Smells–5 W's: Which, When, What, Who, WhereIEEE Transactions on Software Engineering10.1109/TSE.2018.288097747:1(17-66)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TSE.2018.2880977
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