软件更改可能性预测中的重要性度量研究  

Research on Significance Metrics for Software Change-Proneness Prediction

作　　者：朱晓燕[1] 宋擒豹[1] 张本文[1] 

机构地区：[1]西安交通大学计算机系,西安710049

出　　处：《西安交通大学学报》2013年第12期1-6,共6页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金资助项目(61070006)

摘　　要：针对已有软件度量在对类进行衡量时没有考虑软件的整体结构的问题,考虑到类在整个软件项目中的重要性,提出了2个新的软件度量指标,并以此构造了一种预测效果更好的软件更改可能性预测模型。新的软件度量指标将软件项目用图进行表示,其中节点表示项目中的类,边表示类之间的依赖关系;在整个软件项目构建的图中对类的重要性进行研究,从而构造预测软件更改可能性的重要性度量。选用了6个开源软件项目进行实验验证,实验数据包括:①项目中每个类的软件度量,包括4个基于大小的软件度量指标,6个基于复杂度的软件度量指标和2个基于重要性的软件度量指标;②项目中每个类在选定的历史阶段的更改行数。实验结果表明,新的软件度量指标可使5个项目的精度得到提高,平均提高1.16%,并使6个项目的曲线下面积得到提高,平均提高3.65%。Since existing software metrics do not consider the whole structure of a software project in evaluating classes, two new static metrics based on significance are proposed to build a better software change-proneness prediction model. The new metrics represent a project as a graph with classes being vertices and dependencies between classes being edges. The importance of a class is evaluated in the whole structure of the project, and the significance metrics for software change-proneness prediction are then proposed. Six open source software projects are used to validate the new metrics. Data used in experiments include two parts. The first part is metric of each class, including 4 volume metrics, 6 complexity metrics and 2 significance metrics, and the second part is the number of changed lines of each class during the selected history period. Experimental results show that when the new metrics are combined with other metrics, the accuracy increases on 5 projects by 1.16% on average, and the area under ROC curve increases on 6 projects by 3.65 ~/~0 on average.

关 键 词：软件度量 更改可能性 预测模型 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]