似然关系模型在航天软件缺陷预测中的应用

陈媛; 沈湘衡; 王安邦; 宋元章

doi:10.3788/OPE.20132107.1865

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似然关系模型在航天软件缺陷预测中的应用

信息科学 | 更新时间：2020-08-12

- 似然关系模型在航天软件缺陷预测中的应用
- Application of probabilistic relational model to aerospace software defect prediction
- 光学精密工程 2013年21卷第7期页码：1865-1872
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所,吉林长春,130033
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目-激光共性技术研究()
- DOI：10.3788/OPE.20132107.1865
  中图分类号： TP311.5
- 收稿日期：2012-08-02，
  
  修回日期：2012-10-22，
  
  网络出版日期：2013-07-15，
  
  纸质出版日期：2013-07-15
- 稿件说明：
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陈媛沈湘衡王安邦宋元章. 似然关系模型在航天软件缺陷预测中的应用[J]. 光学精密工程, 2013,21(7): 1865-1872

. Application of probabilistic relational model to aerospace software defect prediction[J]. Editorial Office of Optics and Precision Engineering, 2013,21(7): 1865-1872
陈媛沈湘衡王安邦宋元章. 似然关系模型在航天软件缺陷预测中的应用[J]. 光学精密工程, 2013,21(7): 1865-1872 DOI： 10.3788/OPE.20132107.1865.

. Application of probabilistic relational model to aerospace software defect prediction[J]. Editorial Office of Optics and Precision Engineering, 2013,21(7): 1865-1872 DOI： 10.3788/OPE.20132107.1865.

摘要

将似然关系模型在描述和推理多属性类之间关系及其不确定性知识方面的优势用于预测软件缺陷，提出了航天软件缺陷预测模型PRM_METHOD。首先，提出了基于软件测试的软件缺陷分类方法，以软件缺陷类关系为例分析了似然关系模型用于航天软件缺陷预测的理论依据；然后，在对人员能力、缺陷数量特征等数据进行定义和泛化等预处理的基础上，描述了提出的预测模型PRM_METHOD，详细阐述其结构、学习过程以及预测过程，并针对数据集的分类操作提出了基于弥合数据缝隙的k-均值聚类方法。最后，以某航天项目软件为例验证了模型PRM_METHOD的实现过程，并以实际测试工作中产生的历史数据作为训练集和验证集进行实验验证。验证结果显示，验证集的记录与预测结果的平均绝对偏差均值为0.086 8，即模型的预测精度为0.913 2，表明该模型对关联关系较为复杂的航天软件缺陷有较好的预测精度。

Abstract

An aerospace software defect prediction model PRM_METHOD was proposed by use of the advantage of probabilistic relational model in describing and reasoning the relationship between multi-attribute classes and their uncertainty knowledge. First

a software defect classification method based on software test was proposed

and the theoretical basis of the application of probabilistic relational model to the aerospace software defect prediction was analyzed via the relationship between software defect classes. Then

under the definition and generalization of staff capacity and the feature of defect quantity

the model PRM_METHOD was described with its structure

learning and predict process. Moreover

an improved k-average clustering algorithm based on closing data gap was proposed aim at data set classification operation. Finally

an aerospace software was taken as the example to actualize the model

and the practical testing data were used as the training set and validation set to validate it as well. The results show that the average of mean absolute deviation between the validation set and predict result is 0.086 8

which means the prediction accuracy of the model is 0.913 2. Therefore

the conclusion is that the model PRM_METHOD has better prediction accuracy to the aerospace software defect prediction with a more complex associated relationship.

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references

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