Increasing the accuracy of software fault prediction using majority ranking fuzzy clustering
Although many machine-learning and statistical techniques have been proposed widely for defining fault prone modules during software fault prediction, but this area have yet to be explored as still there is a room for stable and consistent model with high accuracy. In this paper, a new method is pro...
Saved in:
Main Authors: | Abaei, Golnoush, Selamat, Ali |
---|---|
Format: | Article |
Published: |
Springer Verlag
2015
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/59320/ http://dx.doi.org/10.1007/978-3-319-10389-1_13 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Software fault prediction based on improved fuzzy clustering
by: Abaei, Golnoosh, et al.
Published: (2014) -
Software fault prediction using BP-based crisp artificial neural networks
by: Abaei, Golnoush, et al.
Published: (2015) -
An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction
by: Abaei, Golnoush, et al.
Published: (2015) -
A fuzzy logic expert system to predict module fault proneness using unlabeled data
by: Abaei, Golnoush, et al.
Published: (2020) -
Increasing the accuracy of software development effort estimation using projects clustering
by: Bardsiri, V. Khatibi, et al.
Published: (2012)