Analysis of Object Oriented Metrics on a Java Application

Amalarethinam, D. I. George; Hameed, P.H. Maitheen Shahul

doi:10.5120/ijca2015905226

Cited by 2 publications

(1 citation statement)

References 7 publications

(6 reference statements)

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“…For example, lines of code, weighted methods per class, and the coupling between objects. whereas dynamic metrics refer to data collected from the runtime behavior of software, e.g., dynamic coupling, dynamic lack of cohesion, and dynamic coupling between objects, these metrics aim to evaluate the design of the object-oriented application, rather than the implementation of the system [28] selected metrics in this study are a large set of object-oriented metrics that are considered as independent variables as shown in Table 4. All these metrics have been calculated through software tools, which analyzes the source code of Java projects using the eclipse JDT library.…”

Section: Software Metrics Extractionmentioning

confidence: 99%

Deep convolutional neural network model for bad code smells detection based on oversampling method

Khleel

Nehéz²

2022

IJEECS

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Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.</span>

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Section: Software Metrics Extractionmentioning

confidence: 99%