Implementation and Evaluation of Optimized Algorithm for Software Architectures Analysis through Unsupervised Learning (Clustering)

被引：0

作者：

Khan, Qadeem ^{[1
]}

Akram, Usman ^{[1
]}

Butt, Wasi Haider ^{[1
]}

Rehman, Saad ^{[1
]}

机构：

[1] Natl Univ Sci & Technol, Dept Comp Engn, Coll Elect & Mech Engn, Rawalpindi, Pakistan

来源：

2016 17TH INTERNATIONAL CONFERENCE ON SCIENCES AND TECHNIQUES OF AUTOMATIC CONTROL AND COMPUTER ENGINEERING (STA'2016) | 2016年

关键词：

Software Architecture; Data Mining; Software Clustering; Algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Software projects are getting more complex and thus it is very difficult for the companies to develop their projects alone. There are multiple heterogeneous systems which are different by multiple perspectives such as different users functionalities delivered different CASE tools, technology adopted for software development, and different platform used for deployment. Thus heterogeneous systems are very difficult to be analyzed and maintained. In this research, we are going to perform the architecture analysis through data mining approach. There are many algorithms used in data mining for analysis of data such as: Association, Sequential Pattern, Similar Sequences, Demographics Clustering, Neural Networks, and Decision Tree. In this research, we optimized these algorithms through proposing our own algorithm for clustering software architectures. Since the data set of software architecture is not available so hence we designed our own data set for this research.

引用

页码：266 / +

页数：7

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