Software Fault Estimation Framework based on aiNet

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Qian Yin
AU  - Ruiyi Luo
AU  - Ping Guo
PY  - 2014
DA  - 2014/08/01
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  - 10.1080/18756891.2013.858907
ID  - Yin2014
ER  -