Informatics Publishing Limited

Jaspreet kaur ¹, Satwinder Singh ²

Affiliations
1 C. S. E. and I. T. Department B. B. S. B. E. C, Fatehgarh Sahib, Punjab – 140407, IN
2 Centre for Computer Science and Technology, Central university of Punjab, Bathinda, IN

Abstract

Objectives of the Study (a) To study previously designed models for identification of refactoring area in Object Oriented Software Systems. (b) To design a general framework or model that helps to easily identify the software code smells for a good quality of coding. (c) To identify the bad smells in the code with a design of neural network based model with the help of object-oriented metrics and further to predict the performance of the proposed model using various evaluation parameters of confusion matrix. Analysis/Methods: In this study, two different versions of Rhino (1.7r1 and 1.7r2) were taken as dataset. Object –Oriented metrics were taken as input data and the probability factor (occurrence or non-occurrence of a bad smell as output. Presence of a bad smell was considered as 1 and 0 means absence of bad smell. If there was at least one bad smell present in the code in a class, it was marked as smelly class. The tool used to extract the databases for collected object –oriented metrics and bad smells of these Rhino versions is PTIDEJ. Further, the data was tested on neural networks for different epochs to predict their performance. Findings: a) Bad Smell Analysis: Twelve design smells were considered to detect the presence of bad smell in code. If there was at least one bad smell present in the code in a class, it was marked as smelly class. b) Neural Network Model Table: Weight and bias factor for various predictors were calculated for different epochs (500, 1000, and 2000). It shows the weights assigned from input layer to hidden layer and from hidden layer to output neurons layer. After the training, the weights were tested on various datasets. C) Performance Tables and Graphs: In this, the Neural network proposed model was trained using different number of epochs to examine if the number of epochs used in training has any impact on the results or not. Further, the results for the accuracy of these models were shown. Novelty/Improvement: When the data was highly trained then the results were better. When the data was trained with 500 epochs, it was suitable for only with-in company projects but when the data was more trained than the model was also appropriate for cross projects. It was seen that when the data was trained with 1000 and 2000 epochs, the results of the proposed model were improved.

Keywords

Artificial Neural Networks (ANN),Bad smells,Logistic regression,Object Oriented Metrics, Refactoring, Software maintainability

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Neha Kumari ¹, Satwinder Singh ¹

Affiliations
1 Department of Computer Science and Technology, Central University of Punjab, City Campus, Manasa Road, Bathinda – 151001, Punjab, IN

Abstract

Objectives: To build a model for the prediction of the code smells using the supervised learning techniques. The motive to build code smell prediction model is to propose a model with less false positive code smells. Also, the proposed model is cross validated using 10-fold cross validation Methods/Statistical Analysis: To build a smell prediction model, two code smell detection tools are used i.e. IPlasma and PMD. The metrics are extracted using understand and IPlasma. To achieve the above mentioned objective, two experiments are performed. One is using the code smells of the PMD and the other one is using the smells of the IPLasma. The smells of the PMD are associated with the metrics that are extracted using Understand. Then, the model is trained using the different supervised learning algorithms that are called classifiers i.e. Random Forest, Naïve Bayes and Kstar. Findings: In this research work, two experiments are performed. One is using the code smells of the PMD and other one is using the code smells of the IPlasma. From the results obtained i.e. with the code smells of the PMD, it is concluded that Random Forest predicts small number of false positive and false negative code smells as the precision and Recall of the Random Forest in each dataset is larger than the other two classifier’s. Moreover, the ROC value of Random Forest is higher in some datasets and in some datasets the ROC value of KStar is higher. The results obtained i.e. with the code smells of the IPlasma, it is concluded that again Random Forest predict code smells more correctly than the other two classifiers and give less number of false positive and false negative code smells. Moreover, there is an exception, there is one dataset in which Random Forest and KStar both shows 100% accuracy i.e., Precison and Recall both are equal to 1, which shows that both classifiers predicts no false positive and false negative code smells. Moreover, the ROC value of Random Forest is higher than the other two classifiers, even in some datasets it is equals to 1. Using this it is concluded that Random Forest gives the best code smell predicting model. Application/Improvements: The results of this experiments shows only one case where the false positive and false negative code smells are not predicted by the models. This can improve such that on apply each and every dataset it gives zero false positive code smells.

Keywords

Automatic Tools, Bad Code Smells, Code Smell, Code Smell Prediction, Supervised Learning Techniques

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