Department Of Computer Science And Technology

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Author: search.filters.author.Singh, SatwinderSubject: search.filters.subject.Object oriented metrics

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Now showing 1 - 3 of 3
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    Code clone detection and analysis using software metrics and neural network: A Literature Review
    (Eighth Sense Research Group, 2015) Kumar, Balwinder; Singh, Satwinder
    Code clones are the duplicated code which degrade the software quality and hence increase the maintenance cost. Detection of clones in a large software system is very tedious tasks but it is necessary to improve the design, structure and quality of the software products. Object oriented metrics like DIT, NOC, WMC, LCOM, Cyclomatic complexity and various types of methods and variables are the good indicator of code clone. Artificial neural network has immense detection and prediction capability. In this paper, various types of metric based clone detection approach and techniques are discussed. From the discussion it is concluded that clone detection using software metrics and artificial neural network is the best technique of code clone detection, analysis and clone prediction
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    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.
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    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.