International Journal of Computational Intelligence Systems

Volume 7, Issue 4, August 2014, Pages 715 - 723

Software Fault Estimation Framework based on aiNet

Authors
Qian Yin, Ruiyi Luo, Ping Guo
Corresponding Author
Qian Yin
Available Online 1 August 2014.
DOI
https://doi.org/10.1080/18756891.2013.858907How to use a DOI?
Keywords
software fault prediction, aiNet, testing, framework
Abstract
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 4
Pages
715 - 723
Publication Date
2014/08
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.858907How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Qian Yin
AU  - Ruiyi Luo
AU  - Ping Guo
PY  - 2014
DA  - 2014/08
TI  - Software Fault Estimation Framework based on aiNet
JO  - International Journal of Computational Intelligence Systems
SP  - 715
EP  - 723
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.858907
DO  - https://doi.org/10.1080/18756891.2013.858907
ID  - Yin2014
ER  -