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SQA by Defect prediction: An SVM based In-Appendage Software Development Log Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 43 - Number 24 |
| Year of Publication: 2012 |
| Authors: N. Rajasekhar Reddy, R. J. Ramasree, R. MD. Shafi |
10.5120/6437-8839
|
N. Rajasekhar Reddy, R. J. Ramasree, R. MD. Shafi . SQA by Defect prediction: An SVM based In-Appendage Software Development Log Analysis. International Journal of Computer Applications. 43, 24 ( April 2012), 15-22. DOI=10.5120/6437-8839
Abstract
The present paper proposes a Machine learning technique for defect forecasting and handling for SQA called appendage log training and analysis, can be referred as ALTA. The proposed defect forecasting of in-appendage software development logs works is to deal the forecasted defects accurately and spontaneously while developing the software. The present proposed mechanism helps in minimizing the difficulty of SQA. The overall study is conducted on evaluating the proposed model which indicates the defect forecasting in-appendage software development log training and analysis is significant growth to lessen the complexity of Software Quality Assessment.
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