Open AccessSoftware Defect Prediction u sing Support Vectorised Data and Intelligent Techniques
Author(s) -
Kannaiyan Sathish Kumar,
Ch. G. V. N. Prasad
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2124.039520
Subject(s) - computer science , software , software bug , context (archaeology) , data mining , software engineering , software development , operating system , paleontology , biology
Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.