Understanding the Evolution of Code Smells by Observing Code Smell Clusters

Tahmid, Ahmad; Nahar, Nadia; Sakib, Kazi

doi:10.1109/saner.2016.45

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…Looking at the results, we can observe that for all the code smells but Misplaced Class technical control factors such as LOC, CBO, Code Churn, and number of commits between two releases are the ones that more closely influence the code smell intensity. These results were quite expected, since previous work has shown there exist technical aspects of source code (e.g., LOC) that "naturally" influence the evolution of code smells [115], [116]. At the same time, also the previous intensity of a code smell plays an important role when explaining the future intensity: likely, this is a reflection of the fact that developers generally tend to not refactor code smells and, when they do, the refactoring is most of the times not effective [13], thus increasing the intensity of smells over time.…”

Section: Analysis Of the Resultssupporting

confidence: 60%

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba

Tamburri

Fontana

et al. 2021

IIEEE Trans. Software Eng.

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Section: Analysis Of the Resultssupporting

confidence: 60%

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba

Tamburri

Fontana

et al. 2021

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…Looking at the results in Table 1, we observe that for all the code smells but Misplaced Class traditional metrics such as LOC, CBO, and Code Churn influence the code smell intensity most. These results were quite expected given the importance of LOC for evolution of code smells [11]. Also the previous intensity of a smell is important: likely, this is caused by developers rarely refactoring code smells [1], thus increasing the intensity of smells over time.…”

Section: Statistical Modelingmentioning

confidence: 65%

How do community smells influence code smells?

Palomba

Tamburri

Serebrenik

et al. 2018

Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

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Code smells reflect sub-optimal patterns of code that often lead to critical software flaws or failure. In the same way, community smells reflect sub-optimal organisational and socio-technical patterns in the organisational structure of the software community. To understand the relation between the community smells and code smells we start by surveying 162 developers of nine opensource systems. Then we look deeper into this connection by conducting an empirical study of 117 releases from these systems. Our results indicate that community-related factors are intuitively perceived by most developers as causes of the persistence of code smells. Inspired by this observation we design a communityaware prediction model for code smells and show that it outperforms a model that does not consider community factors. CCS CONCEPTS • Software and its engineering → Software organization and properties;

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“…It was analyzed that the differences in the similarity index were 30% that occurred when compared versions of code segments. Tahmid, A et al,2016 [21] aimed to identify the development of the code smell in S/W by observing the behavior of the clusters like as size, quantity and connectivity. The clusters and its features were examined.…”

Section: Smell 5: Test Code Doublingmentioning

confidence: 99%

Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review

Tewatia¹,

Kumar²

2020

IJCA

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Test case generation in terms of code smell refers to the features of the software that recognizes a code and design issues which make software hard to realize, evolve and preserve code. Generally, the maintenance and detection of the software applications become more difficult due to the presence of the smell. Programmers are unable to identify the source code applications and face issues to understand the source code of the project. Simultaneously, a code smell face problem to refractors and developers for upgrading and maintenance of the source code. Present, research is active in the automated detection and testing of the bad code smell. Without the knowledge of the code smell with diverse refactoring, and efficient tool make the detection of the code difficult. Particularly, the code smell in software is based on the programming of the source code, that may lead to difficulty in detection of bad code smell. This paper analysis the detection tools, method of code smells and methods for the detection of bad test code smell. The categories of different test code smells are described which includes applications, classes and different method-level code smells. Moreover, detail definition of the bad smell in source code and its types in source code is also elaborated. In addition to that, bad code smell detection is described which includes automated detection and machine learning methods for identifying the bad code smell. Additionally, the automated tools which are given as, a check style, décor, infusion, deodorant and iplasma. The detection methods are decision tree (DT), Learning group rules, Multilayer perceptron (MLP), Naïve Bayes (NB), Support Vector Machine (SVM), Radial basis system (RBS) network.

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Understanding the Evolution of Code Smells by Observing Code Smell Clusters

Cited by 9 publications

References 7 publications

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

How do community smells influence code smells?

Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review

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