Software Quality Prediction using Random Particle Swarm Optimization (PSO)

被引：0

作者：

Ali, Asif ^{[1
]}

Choudhary, Kavita ^{[1
]}

Sharma, Ashwini ^{[2
]}

机构：

[1] Jagannath Univ Raj, Comp Sci & Engn, Jaipur, Rajasthan, India

[2] GEC Jhalawar Raj, Comp Sci & Engn, Jhalawar, India

来源：

PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON CIRCUIT, POWER AND COMPUTING TECHNOLOGIES (ICCPCT 2016) | 2016年

关键词：

Software quality; Clustering; Chi-Square Test; FM; PO; OR; PSO;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper we have considered java based modules as a dataset for software quality prediction. The properties used are class, object, inheritance and dynamic behavior. The data modularity considered for this work is 1-10 and 11-20. First the data is arranged in the group and then it is tested based on chi-square test. Then we have calculated F-measure (FM), Power (PO) and Odd Ratio (OR) and find the parametric quality of software metrics. Then we have applied random particle swarm optimization for testing the optimized value and the obtained results found are improved.

引用

页数：6

共 26 条

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