{"title":"A method for developing node probability table using qualitative value of software metrics","authors":"Chandan Kumar, D. Yadav","doi":"10.1109/C3IT.2015.7060187","DOIUrl":null,"url":null,"abstract":"Recently, Bayesian Belief Network (BBN) becomes one of the most popular choices for uncertainty modeling and has been widely used in software engineering such as defect prediction, reliability and quality prediction, testing effort prediction and software risk assessment. The Node Probability Tables (NPT) play a vital role in BBN. Failure data is not available in the early phases (i.e., phases which occur before testing phase) of software development life cycle (SDLC). However, metrics of early phases of SDLC can be assessed qualitatively. Therefore, an intelligent selection of software metrics also plays a vital role in developing a software defect prediction model using BBN. In this paper, a technique has been proposed to develop the NPT of a BBN using the qualitative value of software metrics.","PeriodicalId":402311,"journal":{"name":"Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/C3IT.2015.7060187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Recently, Bayesian Belief Network (BBN) becomes one of the most popular choices for uncertainty modeling and has been widely used in software engineering such as defect prediction, reliability and quality prediction, testing effort prediction and software risk assessment. The Node Probability Tables (NPT) play a vital role in BBN. Failure data is not available in the early phases (i.e., phases which occur before testing phase) of software development life cycle (SDLC). However, metrics of early phases of SDLC can be assessed qualitatively. Therefore, an intelligent selection of software metrics also plays a vital role in developing a software defect prediction model using BBN. In this paper, a technique has been proposed to develop the NPT of a BBN using the qualitative value of software metrics.
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