A Systematic Literature Review: Code Bad Smells in Java Source Code

Gupta, Aakanshi; Suri, Bharti; Misra, Sanjay

doi:10.1007/978-3-319-62404-4_49

Cited by 30 publications

(14 citation statements)

References 59 publications

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“…Gupta et al [20] performed a SLR based on publications from 1999 to 2016 and 60 papers, screened out of 854, are deeply analyzed. The objectives of this SLR were to provide an overview of the investigation carried out in the area of CS, identify the techniques used in the detection and find out which CS are the most detected in the various detection approaches.…”

Section: Related Workmentioning

confidence: 99%

Code Smells Detection and Visualization: A Systematic Literature Review

Reis

Abreu

Carneiro

et al. 2021

Arch Computat Methods Eng

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Context: Code smells tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Researchers have argued that due to the subjectiveness of the code smells detection process, proposing an effective use of automatic support for this end is a non trivial task.Objective: This Systematic Literature Review (SLR) has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former.Method: Over eighty primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them.Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), metric-based (24.1%), and symptom-based approaches (19.3%). Most of the studies (83.1%) use open-source software, with

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Section: Related Workmentioning

confidence: 99%

Code Smells Detection and Visualization: A Systematic Literature Review

Reis

Abreu

Carneiro

et al. 2021

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…They stated that mostly the bad smells persist up to the latest analyzed release accumulated as the project matured. A variety of bad smell detection techniques such as binary logistic regression, clustering, genetic algorithm, and relation association rule mining have been tabulated in the literature [ 13 ].…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Previous studies on bad smells and design errors offered many detection techniques [ 13 ], including expert-based approach [ 14 ] and logical prediction of bad smells using machine learning techniques [ 12 ]. In an existing software, a bad smell can be detected simply by using a tool and actions can be taken accordingly.…”

Section: Introductionmentioning

confidence: 99%

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Gupta¹,

Suri

Kumar³

et al. 2018

Entropy

Self Cite

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Abstract:The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

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“…Nonetheless, the prediction performance of SDP models depends on the quality of software metric datasets used for developing the models. That is, software features used for building SDP models influence the prediction performance of SDP models [ 4 , 9 , 22 , 23 ]. These software features are convoluted and distorted which can be traced to class imbalance problem.…”

Section: Introductionmentioning

confidence: 99%

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

Balogun

Lafenwa-Balogun

Mojeed

et al. 2020

Lecture Notes in Computer Science

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Class imbalance is a prevalent problem in machine learning which affects the prediction performance of classification algorithms. Software Defect Prediction (SDP) is no exception to this latent problem. Solutions such as data sampling and ensemble methods have been proposed to address the class imbalance problem in SDP. This study proposes a combination of Synthetic Minority Oversampling Technique (SMOTE) and homogeneous ensemble (Bagging and Boosting) methods for predicting software defects. The proposed approach was implemented using Decision Tree (DT) and Bayesian Network (BN) as base classifiers on defects datasets acquired from NASA software corpus. The experimental results showed that the proposed approach outperformed other experimental methods. High accuracy of 86.8% and area under operating receiver characteristics curve value of 0.93% achieved by the proposed technique affirmed its ability to differentiate between the defective and non-defective labels without bias.

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A Systematic Literature Review: Code Bad Smells in Java Source Code

Cited by 30 publications

References 59 publications

Code Smells Detection and Visualization: A Systematic Literature Review

Code Smells Detection and Visualization: A Systematic Literature Review

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

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