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Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

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成果类型:
期刊论文
作者:
Jin, Cong*;Jin, Shu-Wei
通讯作者:
Jin, Cong
作者机构:
[Jin, Cong] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Jin, Shu-Wei] Ecole Normale Super, Dept Phys, F-75231 Paris 5, France.
通讯机构:
[Jin, Cong] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
ANN;Software metrics;Fault-prone prediction;QPSO
期刊:
Applied Soft Computing
ISSN:
1568-4946
年:
2015
卷:
35
页码:
717-725
基金类别:
science and technology research program of Wuhan of China [201210121023]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPS...

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