Software Defect Prediction based on Adaboost algorithm under Imbalance Distribution

DOI

10.2991/icsma-16.2016.128How to use a DOI?

Keywords

Software defect prediction, Adaboost, Neural Network, Imbalance distribution.

Abstract

Software defects will lead to software running error and system crashes. Many methods were proposed to solve this problem. However, the imbalance distribution of software defects leads to the major bias and accuracy loss for most software defect prediction methods. In this paper, we propose an application which combine Adaptive Boosting(AdaBoost) and Back-propagation Neural Network(BPNN) algorithm to train software defect prediction model. BPNN was utilized as a weak leaner in AdaBoost and tweaked in favor of instances misclassified. The experiments show that the proposed method in the paper significantly improves the performance than the previous models, which is effective to deal with the imbalance software defect data.

Copyright

© 2016, 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  - CONF
AU  - Yan Gao
AU  - Chunhui Yang
PY  - 2016/12
DA  - 2016/12
TI  - Software Defect Prediction based on Adaboost algorithm under Imbalance Distribution
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 739
EP  - 746
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.128
DO  - 10.2991/icsma-16.2016.128
ID  - Gao2016/12
ER  -