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Software Fault Prediction Using Unsupervised Learning Technique: A Practical Approach
Ekbal Rashid 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.11
Unsupervised learning techniques such as clustering can be used in software fault prediction, where fault labels are not available. The accurate prediction of faults is likely to occur in coding and that can be rectified early testing phase, which reduces the testing cost as well as maintenance cost and enhance the quality of software. In respect of data mining approach, if training data are not present, then I can not use the supervised learning, this is the biggest problem. To solve this problem, new models using unsupervised learning such as clustering algorithms are quite necessary. This work is the extended version of a Various Strategies and Technical Aspects of Data Mining: A Theoretical Approach, which moves towards practical implementation from theoretical foundation [1]. The main objective of this work to find whether software is faulty or non faulty by using the confusion matrix and also calculating the False Positive Rate (FPR), False Negative Rate (FNR), and Error or fault in a software module. In order to obtain the results I have used an indigenous tool.
Measuring the Quality of Software on the basis of Time Gap and Quality Gap with Standard Model-II
Ekbal Rashid,Srikanta Patnaik 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
While dealing with students in making their projects, we found that students are ready to cope up the standard software defined by standard body like IEEE/ACM. In this paper we have set the quality value of standard software on the basis of quality function for each month and comparing with standard software and software being developed. The novel idea of this paper is to calculate the difference in quality of the software being developed and the standard software which has been decided upon as the criteria for comparison. The system also calculating how much effort needed and what should be the speed of software being developed. This work is the second part of a Understanding the State of Quality of Software on the basis of Time Gap, Quality Gap and Difference with Standard Model, which moves towards practical implementation from theoretical foundation [1]. In order to obtain results we have used an indigenous tool for calculating the quality gap and time gap at a particular point of time and for the graphical representation of data, we have used Microsoft office 2007 graphical chart. For finding the mean, standard deviation, T value and P value, we have used SPSS version 16.