An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction
Software testing is a crucial task during software development process with the potential to save time and budget by recognizing defects as early as possible and delivering a more defect-free product. To improve the testing process, fault prediction approaches identify parts of the system that are m...
Main Authors: | Abaei, Golnoush, Selamat, Ali, Fujita, Hamido |
---|---|
Format: | Article |
Published: |
Elsevier
2015
|
Subjects: |
Similar Items
-
Increasing the accuracy of software fault prediction using majority ranking fuzzy clustering
by: Abaei, Golnoush, et al.
Published: (2015) -
Software fault prediction using BP-based crisp artificial neural networks
by: Abaei, Golnoush, et al.
Published: (2015) -
Software fault prediction models using machine learning approach /
by: Golnoush Abaei, 1979-, author
Published: (2015) -
Software fault prediction models using machine learning approach /
by: Golnoush Abaei, 1979-, author, et al.
Published: (2015) -
Software fault prediction based on improved fuzzy clustering
by: Abaei, Golnoosh, et al.
Published: (2014)