Feature indicators: A self-organizing map approach to legacy code

被引：0

作者：

Chan, A ^{[1
]}

Spracklen, T ^{[1
]}

机构：

[1] Univ Aberdeen, Dept Engn, Elect Res Grp, Aberdeen AB24 3UE, Scotland

来源：

IC-AI'2000: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 1-III | 2000年

关键词：

self-organizing maps; software reverse engineering; clustering; object orientation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The self-organizing map's unsupervised clustering property, is known for classifying high dimensional data sets into clusters that have similar features. Based on this property, it is demonstrated that a self-organizing map is capable of identifying features within software code by grouping procedures with similar properties together. This permits us to identify potential objects, abstract data types or classes. To further enhance the visualization of clusters by providing a form of justification for the groupings, toe introduce "feature indicators" that labels the map with the elements that most contributed to each fired self-organizing map's unit.

引用

页码：1449 / 1454

页数：6