A Comparative Study on Code Smell Detection Tools

Hamid, Almas; Ilyas, Muhammad; Hummayun, Muhammad; Nawaz, Asad Ali

doi:10.14257/ijast.2013.60.03

Cited by 30 publications

(17 citation statements)

References 11 publications

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“…In this paper, a software which is developed based on the Component-Based Software Engineering (CBSE) is considered having 15 components. The jdeodorant tool is used to identify the code smells from the independent components (Fontana et al, 2012;Hamid et al, 2013;Moha et al, 2010). The relative importance of these code smells is measured by calculating its weights using entropy approach.…”

Section: Frame Work For Ranking the Components As Per The Refactoringmentioning

confidence: 99%

Prioritizing the refactoring need for critical component using combined approach

Sehgal¹,

Mehrotra²,

Bala³

2018

10.5267/j.dsl

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Section: Frame Work For Ranking the Components As Per The Refactoringmentioning

confidence: 99%

Prioritizing the refactoring need for critical component using combined approach

Sehgal¹,

Mehrotra²,

Bala³

2018

10.5267/j.dsl

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“…Most approaches for code smell detection use object-oriented metrics [6,7] to determine if a software system contains bad smells or not. They have identified empirical thresholds for object-oriented metrics by conducting case studies on object-oriented programs [8].As code size increases, automatic detection tools [9] are needed to help the developer by finding code smells systematically. Automatic software tools [10] have been introduced for visually locating code smells in source code by highlighting suspicious code snippets.…”

Section: Related Workmentioning

confidence: 99%

Finding Bad Code Smells with Neural Network Models

Kim

2017

IJECE

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Code smell refers to any symptom introduced in design or implementation phases in the source code of a program. Such a code smell can potentially cause deeper and serious problems during software maintenance. The existing approaches to detect bad smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of software detection tools have been developed, they still have limitations and constraints in their capabilities. In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects as experimental dataset. The most well-known object-oriented metrics are considered to identify the presence of bad smells. The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. The training dataset is used to learn the network model which will predict smelly classes in this study. The optimized network model will be chosen to be evaluated on the test dataset. The experimental results show when the modelis highly trained with more dataset, the prediction outcomes are improved more and more. In addition, the accuracy of the model increases when it performs with higher epochs and many hidden layers.

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“…According to object oriented design heuristics and principles, method must be placed in the class, in which data it manipulates more. This basic heuristic has been used in inCode approach to detect these methods as feature envy code smell [13]. Due to no access to inCodes documentation, the approach is not understandable of how it detect only static methods as smell rather than non-static methods.…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Move Method Refactoring to Optimize Modularization Using Conceptual Similarity

Rahman¹,

Rahman²,

Hossain³

2017

IJITCS

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Abstract-Placement of methods within classes is one of the most important design activities for any object oriented application to optimize software modularization. To enhance interactions among modularized components, recommendation of move method refactorings plays a significant role through grouping similar behaviors of methods. It is also used as a refactoring technique of feature envy code smell by placing methods into correct classes from incorrect ones. Due to this code smell and inefficient modularization, an application will be tightly coupled and loosely cohesive which reflect poor design. Hence development and maintenance effort, time and cost will be increased. Existing techniques deals with only non-static methods for refactoring the code smell and so are not generalized for all types of methods (static and non-static). This paper proposes an approach which recommends "move method" refactoring to remove the code smell as well as enrich modularization. The approach is based on conceptual similarity (which can be referred as similar behavior of methods) between a source method and methods of target classes of an application. The conceptual similarity relies on both static and nonstatic entities (method calls and used attributes) which differ the paper from others. In addition, it compares the similarity of used entities by the source method with used entities by methods in probable target classes. The results of a preliminary empirical evaluation indicate that the proposed approach provides better results with average precision of 65% and recall of 63% after running it on five well-known open projects than JDeodorant tool (a popular eclipse plugin for refactorings).

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A Comparative Study on Code Smell Detection Tools

Cited by 30 publications

References 11 publications

Prioritizing the refactoring need for critical component using combined approach

Prioritizing the refactoring need for critical component using combined approach

Finding Bad Code Smells with Neural Network Models

Recommendation of Move Method Refactoring to Optimize Modularization Using Conceptual Similarity

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