[2012] A hybrid feature selection for fault prediction

Posted by sanghv on 07/09/2013 in Feature selection |

Software fault prediction plays a vital role in software quality assurance. Identifying the faulty modules helps to better concentrate on those modules and helps improve the quality of the software. With increasing complexity of software nowadays feature selection is important to remove the redundant, irrelevant and erroneous data from the dataset. In general, Feature selection is done mainly based on filter and wrapper. In this paper a hybrid feature selection method is proposed which gives a better
prediction than the traditional methods. NASA’s public dataset KC1 available at promise software engineering repository is used. To evaluate the performance of the software fault prediction models Accuracy, Mean absolute error (MAE), Root mean squared error (RMSE) values are used.

Read more…

[2012] A hybrid feature selection for fault prediction



Powered by Facebook Comments

Tags: , , , ,

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Copyright © 2007-2018 SangHv at Academy Of Finance – HVTC All rights reserved.
This site is using the Desk Mess Mirrored theme, v2.5, from BuyNowShop.com.