{"title":"Software quality prediction using random particle swarm optimization (PSO)","authors":"Asif Ali, Kavita Choudhary, Ashwini Sharma","doi":"10.1109/ICCPCT.2016.7530244","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":431894,"journal":{"name":"2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPCT.2016.7530244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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.
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