Empirical Study about Class Change Proneness Prediction using Software Metrics and Code Smells

Proceedings of the 22nd International Conference on Enterprise Information Systems ◽

10.5220/0009410601400147 ◽

2020 ◽

Author(s):

Antonio Martins ◽

Cristiano Melo ◽

José Monteiro ◽

Javam Machado

Keyword(s):

Empirical Study ◽

Software Metrics ◽

Code Smells ◽

Change Proneness

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Empirical study on code smells in iOS applications

Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems ◽

10.1145/3387905.3388597 ◽

2020 ◽

Author(s):

Kristiina Rahkema ◽

Dietmar Pfahl

Keyword(s):

Empirical Study ◽

Code Smells

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An Empirical Study On Software Metrics and Machine Learning to Identify Untrustworthy Code

10.1109/edcc53658.2021.00020 ◽

2021 ◽

Author(s):

Nadia Medeiros ◽

Naghmeh Ivaki ◽

Pedro Costa ◽

Marco Vieira

Keyword(s):

Machine Learning ◽

Empirical Study ◽

Software Metrics

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On the Effectiveness of Concern Metrics to Detect Code Smells: An Empirical Study

Advanced Information Systems Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-319-07881-6_44 ◽

2014 ◽

pp. 656-671 ◽

Cited By ~ 12

Author(s):

Juliana Padilha ◽

Juliana Pereira ◽

Eduardo Figueiredo ◽

Jussara Almeida ◽

Alessandro Garcia ◽

...

Keyword(s):

Empirical Study ◽

Code Smells

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An Empirical Study of Software Metrics

IEEE Transactions on Software Engineering ◽

10.1109/tse.1987.233475 ◽

1987 ◽

Vol SE-13 (6) ◽

pp. 697-708 ◽

Cited By ~ 80

Author(s):

H.F. Li ◽

W.K. Cheung

Keyword(s):

Empirical Study ◽

Software Metrics

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On the Influence of Human Factors for Identifying Code Smells: A Multi-Trial Empirical Study

2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) ◽

10.1109/esem.2017.13 ◽

2017 ◽

Cited By ~ 7

Author(s):

Rafael Maiani de Mello ◽

Roberto Oliveira ◽

Alessandro Garcia

Keyword(s):

Empirical Study ◽

Human Factors ◽

Code Smells ◽

Identifying Code

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Code smells as system-level indicators of maintainability: An empirical study

Journal of Systems and Software ◽

10.1016/j.jss.2013.05.007 ◽

2013 ◽

Vol 86 (10) ◽

pp. 2639-2653 ◽

Cited By ~ 32

Author(s):

Aiko Yamashita ◽

Steve Counsell

Keyword(s):

Empirical Study ◽

System Level ◽

Code Smells

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A large-scale empirical study of code smells in JavaScript projects

Software Quality Journal ◽

10.1007/s11219-019-09442-9 ◽

2019 ◽

Vol 27 (3) ◽

pp. 1271-1314 ◽

Cited By ~ 2

Author(s):

David Johannes ◽

Foutse Khomh ◽

Giuliano Antoniol

Keyword(s):

Empirical Study ◽

Large Scale ◽

Code Smells

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An empirical study of code smells in JavaScript projects

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER) ◽

10.1109/saner.2017.7884630 ◽

2017 ◽

Cited By ~ 16

Author(s):

Amir Saboury ◽

Pooya Musavi ◽

Foutse Khomh ◽

Giulio Antoniol

Keyword(s):

Empirical Study ◽

Code Smells

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Evaluation of Machine Learning Approaches for Change-Proneness Prediction Using Code Smells

Advances in Intelligent Systems and Computing - Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications ◽

10.1007/978-981-10-3153-3_56 ◽

2017 ◽

pp. 561-572 ◽

Cited By ~ 1

Author(s):

Kamaldeep Kaur ◽

Shilpi Jain

Keyword(s):

Machine Learning ◽

Learning Approaches ◽

Code Smells ◽

Change Proneness

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Empirical Study of Software Metrics

Research Journal of Science and Technology ◽

10.5958/2349-2988.2017.00003.1 ◽

2017 ◽

Vol 9 (1) ◽

pp. 17 ◽

Cited By ~ 1

Author(s):

Madhuri Gupta ◽

Arvind Kalia

Keyword(s):

Empirical Study ◽

Software Metrics

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