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review-article

Software smell detection techniques: : A systematic literature review

Authors:

Amjad AbuHassan,

Mohammad Alshayeb,

Lahouari GhoutiAuthors Info & Claims

Journal of Software: Evolution and Process, Volume 33, Issue 3

https://doi.org/10.1002/smr.2320

Published: 03 March 2021 Publication History

Abstract

Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research.

Graphical Abstract

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Identified and collected 145 primary studies (PS) related to smell detection in software design and code.

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Identified several detection techniques categories.

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Observed that 57% of the studies did not use any performace measures.

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41% of the PSs omitted details on the targeted programing language.

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Detection techniques were not validated in 14% of the PSs.

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Only 18% of the studies addressed bad smell detection at the design level.

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Identified a number of open issues.

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Cited By

Prokić SLuburić NSlivka JKovačević A(2024)Prescriptive procedure for manual code smell annotationScience of Computer Programming10.1016/j.scico.2024.103168238:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103168
Alkhomsan MAlshayeb MBaslyman M(2024)Toward a novel taxonomy to capture code smells caused by refactoringScience of Computer Programming10.1016/j.scico.2024.103120236:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103120
Mohammed MAlshayeb MHassine J(2024)A rule-based approach for the identification of quality improvement opportunities in GRL modelsSoftware Quality Journal10.1007/s11219-024-09679-z32:3(1007-1037)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11219-024-09679-z
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Index Terms

Software smell detection techniques: A systematic literature review
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
    2. Software verification and validation
  2. Software notations and tools

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Software: Evolution and Process

Journal of Software: Evolution and Process Volume 33, Issue 3

March 2021

242 pages

ISSN:2047-7473

EISSN:2047-7481

DOI:10.1002/smr.v33.3

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© 2020 John Wiley & Sons, Ltd.

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Published: 03 March 2021

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Prokić SLuburić NSlivka JKovačević A(2024)Prescriptive procedure for manual code smell annotationScience of Computer Programming10.1016/j.scico.2024.103168238:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103168
Alkhomsan MAlshayeb MBaslyman M(2024)Toward a novel taxonomy to capture code smells caused by refactoringScience of Computer Programming10.1016/j.scico.2024.103120236:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.scico.2024.103120
Mohammed MAlshayeb MHassine J(2024)A rule-based approach for the identification of quality improvement opportunities in GRL modelsSoftware Quality Journal10.1007/s11219-024-09679-z32:3(1007-1037)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11219-024-09679-z
Alazba AAljamaan HAlshayeb M(2024)CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detectionEmpirical Software Engineering10.1007/s10664-024-10445-929:3Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1007/s10664-024-10445-9
Alazba AAljamaan HAlshayeb M(2024)Automated detection of class diagram smells using self-supervised learningAutomated Software Engineering10.1007/s10515-024-00429-w31:1Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/s10515-024-00429-w
Kovačević ALuburić NSlivka JProkić SGrujić KVidaković DSladić G(2024)Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#Neural Computing and Applications10.1007/s00521-024-09551-y36:16(9203-9220)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09551-y
Kotti ZGalanopoulou RSpinellis D(2023)Machine Learning for Software Engineering: A Tertiary StudyACM Computing Surveys10.1145/357290555:12(1-39)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3572905
Marchezan LAssunção WHerac EKeplinger FEgyed ALauwerys CLiu AMuccini H(2023)Fulfilling Industrial Needs for Consistency among Engineering ArtifactsProceedings of the 45th International Conference on Software Engineering: Software Engineering in Practice10.1109/ICSE-SEIP58684.2023.00028(246-257)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00028
Gupta RSingh S(2023)A Novel Transfer Learning Method for Code Smell Detection on Heterogeneous Data: A Feasibility StudySN Computer Science10.1007/s42979-023-02157-64:6Online publication date: 28-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02157-6
Alazba AAljamaan HAlshayeb M(2023)Deep learning approaches for bad smell detection: a systematic literature reviewEmpirical Software Engineering10.1007/s10664-023-10312-z28:3Online publication date: 11-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10312-z
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