TY  - JOUR
T1  - A Comparative Analysis of Hybrid Learning over Back Propagation for
Identifying Defective Prone Modules
AU - Maddipati, Satya Srinivas AU - Yesubabu, A. AU - Pradeepini, G. 
JO  - International Journal of Soft Computing
VL  - 12
IS  - 4
SP  - 199
EP  - 203
PY  - 2017
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2017.199.203
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2017.199.203
KW  - Hybrid learning
KW  -back propagation
KW  -adaptive neuro fuzzy inference system
KW  -neural networks
KW  -software defect prediction
KW  -gradient descendent learning
AB  - The quality of software is improved by identifying defective prone modules which is influenced by
various characteristics of software module like lines of code, Halstead metrics and cyclometric complexity
values. There are various prediction models for identifying defective prone modules from these characteristics.
In this study we are comparing neural networks and Adaptive Neuro Fuzzy Inference System (ANFIS) for
software defect prediction. We applied gradient descendent learning for Neural Networks and Hybrid Leaning
for ANFIS. The performance of the models are evaluated by using the metric Area under ROC curve (AuC)
values. In these experiments, we considered Software Defect Prediction Datasets download from NASA
repositories. The results of ANFIS are found satisfactory compared to Neural Networks.
ER  -