Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

Browse

Author: search.filters.author.Singh, SatwinderHas files: Yes

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Neural network based refactoring area identification in Software System with object oriented metrics
    (Indian Society for Education and Environment, 2016) Kaur, Jaspreet; Singh, Satwinder
    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.
  • Thumbnail Image
    Item
    Comparative performance of fault-prone prediction classes with k-means clustering and MLP
    (Association for Computing Machinery, 2016) Singh, Satwinder; Singla, Rozy
    Software defect in today's era is most important in the field of software engineering. Most of the organizations used various techniques to predict defects in their products before they are delivered. Defect prediction techniques help the organizations to use their resources effectively which results in lower cost and time requirements. There are various techniques that are used for predicting defects in software before it has to be delivered. For example clustering, neural networks, support vector machine (SVM) etc. In this paper two defect prediction techniques:-K-means Clustering and Multilayer Perceptron model (MLP), are compared. Both the techniques are implemented on different platforms. K-means clustering is implemented using WEKA tool and MLP is implemented using SPSS. The results are compared to find which algorithm produces better results. In this paper Object-Oriented metrics are used for predicting defects in the software. ? 2016 ACM.
  • Thumbnail Image
    Item
    Classification of defective modules using object-oriented metrics
    (Inderscience Enterprises Ltd., 2017) Singh, Satwinder; Singla, Rozy
    Software defect in today's era is crucial in the field of software engineering. Most of the organisations use various techniques to predict defects in their products before they are delivered. Defect prediction techniques help the organisations to use their resources effectively which results in lower cost and time requirements. There are various techniques that are used for predicting defects in software before it has to be delivered, e.g., clustering, neural networks, support vector machine (SVM). In this paper two defect prediction techniques: K-means clustering and multi-layer perceptron model (MLP) are compared. Both the techniques are implemented on different platforms. K-means clustering is implemented using WEKA tool and MLP is implemented using SPSS. The results are compared to find which algorithm produces better results. In this paper object-oriented metrics are used for predicting defects in the software. Copyright ? 2017 Inderscience Enterprises Ltd.
  • Thumbnail Image
    Item
    A systematic literature review: Refactoring for disclosing code smells in object oriented software
    (Ain Shams University, 2016) Singh, Satwinder; Kaur, Sharanpreet
    Context: Reusing a design pattern is not always in the favor of developers. Thus, the code starts smelling. The presence of "Code Smells" leads to more difficulties for the developers. This racket of code smells is sometimes called Anti-Patterns. Objective: The paper aimed at a systematic literature review of refactoring with respect to code smells. However the review of refactoring is done in general and the identification of code smells and anti-patterns is performed in depth. Method: A systematic literature survey has been performed on 238 research items that includes articles from leading Conferences, Workshops and premier journals, theses of researchers and book chapters. Results: Several data sets and tools for performing refactoring have been revealed under the specified research questions. Conclusion: The work done in the paper is an addition to prior systematic literature surveys. With the study of paper the attentiveness of readers about code smells and anti-patterns will be enhanced. ? 2017.