Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics

Gupta, Aakanshi; Suri, Bharti; Lamba, Lakshay

doi:10.1002/9781119752134.ch18

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…• 38% of studies focused on tools and methods. Studies (Witten and Frank, 2002;Holschuh et al, 2009;Olbrich et al, 2009;Yamashita et al, 2009;Carneiro et al, 2010;Tempero et al, 2010;Macia et al, 2011;Maneerat and Muenchaisri, 2011;Oliveto et al, 2011;Zhang et al, 2011;Fontana et al, 2012a;Peters and Zaidman, 2012;Fontana et al, 2013a;Palomba et al, 2013;Chatzigeorgiou and Manakos, 2014;Wohlin, 2014;Fontana et al, 2016b;Hermans and Aivaloglou, 2016;Palomba et al, 2016;Mansoor et al, 2017;Wang et al, 2018;Azeem et al, 2019;Guggulothu and Moiz, 2020;Pritam et al, 2019;Pritam et al, 2019;Gupta et al, 2021;Jain and Saha, 2021) are found to enhance tools and proposed methodology to identify code smells. This depicts that the primary goal during the study on code smell was the detection of smell and its methodology.…”

Section: Rq3-to Find the Most Deployed Technique To Study Code Smell ...mentioning

confidence: 99%

Study of Code Smells: A Review and Research Agenda

Tandon,

Kumar,

Singh

2024

Int. j. math. eng. manag. sci.

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Code Smells have been detected, predicted and studied by researchers from several perspectives. This literature review is conducted to understand tools and algorithms used to detect and analyze code smells to summarize research agenda. 114 studies have been selected from 2009 to 2022 to conduct this review. The studies are deeply analyzed under the categorization of machine learning and non-machine learning, which are found to be 25 and 89 respectively. The studies are analyzed to gain insight into algorithms, tools and limitations of the techniques. Long Method, Feature Envy, and Duplicate Code are reported to be the most popular smells. 38% of the studies focused their research on the enhancement of tools and methods. Random Forest and JRip algorithms are found to give the best results under machine learning techniques. We extended the previous studies on code smell detection tools, reporting a total 87 tools during the review. Java is found to be the dominant programming language during the study of smells.

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