%0 Generic %1 kaur2013empirical %A Kaur, Ms. Puneet Jai %A Pallavi, Ms. %B 2013 Information Technology and Power Electronics %D 2013 %E Nishizaki, Dr. Shin-ya %I ACEEE (A Computer division of IDES) %K clustering data defect hierarchical mining prediction %T An Empirical Study for Defect Prediction using Clustering %U http://searchdl.org/public/book_series/LSCS/4/582.pdf %X Reliably predicting defects in the software is one of the holy grails of software engineering. Researchers have devised and implemented a method of defect prediction approaches varying in terms of accuracy, complexity, and the input data they require. An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product’s required maintenance 1. A prediction of the number of remaining defects in an inspected artefact can be used for decision making. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. It strives to improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules 2. In this paper, we will discuss clustering techniques are used for software defect prediction. This helps the developers to detect software defects and correct them. Unsupervised techniques may be used for defect prediction in software modules, more so in those cases where defect labels are not available 3.

@conference{kaur2013empirical, abstract = {Reliably predicting defects in the software is one of the holy grails of software engineering. Researchers have devised and implemented a method of defect prediction approaches varying in terms of accuracy, complexity, and the input data they require. An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product’s required maintenance [1]. A prediction of the number of remaining defects in an inspected artefact can be used for decision making. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. It strives to improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules [2]. In this paper, we will discuss clustering techniques are used for software defect prediction. This helps the developers to detect software defects and correct them. Unsupervised techniques may be used for defect prediction in software modules, more so in those cases where defect labels are not available [3].}, added-at = {2014-02-04T11:28:50.000+0100}, author = {Kaur, Ms. Puneet Jai and Pallavi, Ms.}, biburl = {https://www.bibsonomy.org/bibtex/25463dd837df2d47ec23953cece7318e9/idescitation}, booktitle = {2013 Information Technology and Power Electronics}, editor = {Nishizaki, Dr. Shin-ya}, interhash = {e1c680bb0e90f879a073f661708fd4ff}, intrahash = {5463dd837df2d47ec23953cece7318e9}, keywords = {clustering data defect hierarchical mining prediction}, organization = {Institute of Doctors Engineers and Scientists}, publisher = {ACEEE (A Computer division of IDES)}, timestamp = {2014-02-04T11:28:50.000+0100}, title = {An Empirical Study for Defect Prediction using Clustering}, url = {http://searchdl.org/public/book_series/LSCS/4/582.pdf}, year = 2013 }