Comparative performance of fault-prone prediction classes with k-means clustering and MLP
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Date
2016
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Publisher
Association for Computing Machinery
Abstract
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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Keywords
Defects, Forecasting, Multilayer neural networks, Neural networks, Software engineering, Support vector machines, Comparative performance, Defect prediction, Fault-prone, K-means clustering, Object oriented metrics, Software defects, Time requirements, Weka, Object oriented programming
Citation
Singh, S., & Singla, R. (2016). Comparative performance of fault-prone prediction classes with k-means clustering and MLP. Paper presented at the ACM International Conference Proceeding Series.