An Integration System for Software Product Archiving, Analysis, and Retrieving System

在本研究中，我们开发了一个构件搜索系统SPARS-J，它将Java类的源文件视为构件。在该系统中，我们使用了一种新的排序方法来缩小从可复用库中检索到的软件构件的范围。基于构件库的图形表示方法，定义了构件等级模型。在该模型中，软件组件的集合被表示为一个加权有向图，即图中的节点对应于组件，链接节点的边对应于跨组件使用。相似的组件聚集到一个节点中，从而消除重复组件的影响。图中的节点通过它们的权重来排序，这些权重被定义为有向图的相邻矩阵的特征向量的元素。所得到的排名，命名为组件排名，用于确定查询结果的优先顺序，以便用户可以快速看到排名较高的组件。成分排名背后的思想源于对已发表论文的影响因子(称为影响权重)的计算。这种方法已经扩展到对互联网上的Web文档进行排序。SPARS-J已被应用于各种Java程序集合，例如JDK、从互联网下载的程序以及来自两家公司的商业应用程序。结果表明，被其他类(如实现基本和标准数据结构的类)频繁调用的类通常具有较高的排名，而非标准和特殊类通常具有较低的排名。有两家公司使用SPAS-J自动管理他们的软件资产，SPAS-J显示了非常有希望的结果。

In this research, we have developed a component search system, named SPARS-J, which treats the source files of Java classes as components. In the system, we use a novel ranking method to narrow retrieved software components from reusable libraries. We define a component rank model based on a graph representation scheme of the component library. In this model, a collection of software components is represented as a weighted directed graph, i.e., the nodes of the graph correspond to components and the edges linking the nodes correspond to cross component usage. Similar components are clustered into one node so that the effect of duplicated components is removed. The nodes in the graph are ranked by their weights, which are defined as the elements of the eigenvector of an adjacent matrix for the directed graph. The resulting rank, named component rank, is used to prioritize the query result so that highly ranked components are quickly seen by the user. The idea behind component rank originates from computing impact factors (called influence weights) of published papers. This approach has been extended to ranking Web documents on the Internet. SPARS-J has been applied to various collections of Java programs, such as JDK, programs downloaded from the Internet, and business applications from two companies. The results show that a class frequently invoked by other classes (such as those that implement fundamental and standard data structures) generally has a high rank, and that nonstandard and special classes typically have a low ranking. Two companies use SPARS-J for automatic management of their software assets, and SPARS-J shows very promising results.