Investigating the Impact of Code Smells on System's Quality: An Empirical Study on Systems of Different Application Domains

Fontana, Francesca Arcelli; Ferme, Vincenzo; Marino, Alessandro; Walter, Bartosz; Martenka, Pawel

doi:10.1109/icsm.2013.37

Cited by 48 publications

(43 citation statements)

References 20 publications

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“…We did not find any study investigating more than ten smells by using SVN or CVS . Recently, multiple studies have concluded that smells have an adverse impact on software quality …”

Section: Discussion and Open Issuesmentioning

confidence: 71%

“…These smells are largely related to the property of coupling and often misuse or overuse the coupling. The research on smells reported Feature Envy as a maximum number of smells detected, corrected, and/or considered for maintenance (ie, 25 times) as compared to Intensive / Extensive Coupling …”

Section: Resultsmentioning

confidence: 99%

“…DÉCOR is used for the Odds ratio and Fisher's exact test are applied to check detection of code smells the difference between mutated and nonmutated antipatterns 76 AP A set of guidelines is defined for WSDL Easy SOC plug-in developed for detection Manual analysis of smell 61 CS frequency and source code Correlation is used to check the further relationship metrics for the detection of smells 89…”

Section: Apmentioning

confidence: 99%

“…However, these smells are not reported in Fowler's catalog of smells. Several studies under this category of smells ignored Parent Bequest, 17,53,54,61,68,93,100,101,106,109,112 whereas the No Polymorphism and Procedural Class smells were studied in only two studies. 67,71 Similarly, the Cyclic Inheritance smell is reported in only one primary study that clearly shows a research gap for this smell.…”

Section: Figure 12mentioning

confidence: 99%

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A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

et al. 2018

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Summary This systematic literature review paper investigates the key techniques employed to identify smells in different paradigms of software engineering from object‐oriented (OO) to service‐oriented (SO). In this review, we want to identify commonalities and differences in the identification of smells in OO and SO systems. Our research method relies on an automatic search from the relevant digital libraries to find the studies published since January 2000 on smells until December 2017. We have conducted a pilot and author‐based search that allows us to select the 78 most relevant studies after applying inclusion and exclusion criteria. We evaluated the studies based on the smell detection techniques and the evolution of different methodologies in OO and SO. Among the 78 relevant studies selected, we have identified six different studies in which linguistic source code analysis received less attention from the researchers as compared to the static source code analysis. Smells like the yo‐yo problem, unnamed coupling, intensive coupling, and interface bloat received considerably less attention in the literature. We also identified a catalog of 30 smells infrequently reported for SO systems and that require further attention. Moreover, a suite of 20 smells reported for SO systems can also be detected using static source code metrics in OO. Finally, our review highlighted three major research trends that are further subdivided into 20 research patterns initiating the detection of smells toward their correction.

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“…We did not find any study investigating more than ten smells by using SVN or CVS . Recently, multiple studies have concluded that smells have an adverse impact on software quality …”

Section: Discussion and Open Issuesmentioning

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Apmentioning

confidence: 99%

Section: Figure 12mentioning

confidence: 99%

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A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

et al. 2018

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“…We considered code smells among the ones having the highest frequency (Zhang et al 2011), that may have the greatest negative impact (Olbrich et al 2010) on software quality, and having detection rules defined in the literature or implemented in available tools (Arcelli Fontana et al 2012). We decided to focus our attention on two smells at method level and two smells at class level as reported in Table 2, which correspond to four smells among the most frequent ones, as identified in a study on the frequency of 17 code smells on 76 systems (Arcelli Fontana et al 2013a).…”

Section: Code Smells Definitionsmentioning

confidence: 99%

Comparing and experimenting machine learning techniques for code smell detection

et al. 2015

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Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

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Improving the Quality of Open Source Software

Kaur¹,

Singh²

2023

Agile Software Development

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Investigating the Impact of Code Smells on System's Quality: An Empirical Study on Systems of Different Application Domains

Cited by 48 publications

References 20 publications

A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

Comparing and experimenting machine learning techniques for code smell detection

Improving the Quality of Open Source Software

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