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  • 标题:Comparision between Quad tree based K-Means and EM Algorithm for Fault Prediction
  • 本地全文:下载
  • 作者:Swapna M. Patil ; R.V.Argiddi
  • 期刊名称:International Journal of Computer Science and Information Technologies
  • 电子版ISSN:0975-9646
  • 出版年度:2014
  • 卷号:5
  • 期号:6
  • 页码:7984-7988
  • 出版社:TechScience Publications
  • 摘要:Fault prediction will give one more chance to the development team to retest the modules or files for which the defectiveness probability is high. By spending more time on the defective modules and no time on the non-defective ones, the resources of the project would be utilized better and as a result, the maintenance phase of the project will be easier for both the customers and the project owners. Software fault prediction decreases the total cost of the project and increases the overall project success rate. The perfect prediction of where faults are likely to occur in code can help direct test effort, reduce costs and improve the quality of software. This Paper shows specific methods of fault prediction for software safety that directly address the root causes of software Faults and improve the quality of software.
  • 关键词:Quad Tree; K-Means clustering; Expectation;Maximization Algorithm; Iris Dataset; Clustering; Classification.
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