基于GMDH因果关系的软件缺陷预测模型

软件缺陷预测是软件可靠性研究的一个重要方向。基于自组织数据挖掘(GMDH)网络与因果关系检验理论提出了一种软件缺陷预测模型,借鉴Granger检验思想,利用GMDH网络选择与软件失效具有因果关系的度量指标,建立软件缺陷预测模型。该方法从复杂系统建模角度研究软件度量指标与软件缺陷之间的因果关系,可以检验多变量之间在非线性意义上的因果关系。最后基于两组真实软件失效数据集,将所提出的方法与基于Granger因果检验的软件缺陷预测模型进行比较分析。结果表明,基于GMDH因果关系的软件缺陷预测模型比Granger因果检验方法具有更为显著的预测效果。

Software Defect Prediction Model Based on GMDH Causal Relationship

Software defect prediction is an important aspect in the field of software reliability research.In this paper,we presented a software defect prediction model based on GMDH networks and causal test theory.The model selects the software metrics with the defect causal relationship by learning Granger test ideas and uses the GMDH network which can check the non-linear causality between multiple factors of software defect.Finally,based on two real software failure data sets,we designed an experiment to compare the proposed method with the Granger test software defect prediction model.The experiment results show that the proposed model is more effective and efficient than Granger test software defect prediction model.