Volume 7, Issue 4, August 2014, Pages 715 - 723
Software Fault Estimation Framework based on aiNet
Qian Yin, Ruiyi Luo, Ping Guo
Available Online 1 August 2014.
- https://doi.org/10.1080/18756891.2013.858907How to use a DOI?
- software fault prediction, aiNet, testing, framework
- 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.
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 -