@article{ gras_end--end_2004,
  title = {End-to-end defect modeling},
  volume = {21},
  issn = {0740-7459},
  doi = {10.1109/MS.2004.1331312},
  abstract = {In this context, computer models can help us predict outcomes and anticipate with confidence. We can now use cause-effect modeling to drive software quality, moving our organization toward higher maturity levels. Despite missing good software quality models, many software projects successfully deliver software on time and with acceptable quality. Although researchers have devoted much attention to analyzing software projects' failures, we also need to understand why some are successful - within budget, of high quality, and on time-despite numerous challenges. Restricting software quality to defects, decisions made in successful projects must be based on some understanding of cause-effect relationships that drive defects at each stage of the process. To manage software quality by data, we need a model describing which factors drive defect introduction and removal in the life cycle, and how they do it. Once properly built and validated, a defect model enables successful anticipation. This is why it's important that the model include all variables influencing the process response to some degree.},
  number = {5},
  journal = {IEEE Software},
  author = {Gras, J.-J.},
  month = {September},
  year = {2004},
  keywords = {010, 05, 100, 210, 415, 416, 460, 65, Bayesian methods, Databases, Gras, J., Journal paper, Predictive models, Project management, Software Quality, Software development management, Software measurement, Software systems, System testing, Time measurement, Using cause-effect modeling to drive software quality can move your organization to higher maturity levels., _done, _meta, cause-effect analysis, cause-effect model, cause-effect modeling, end-to-end defect modeling, process control, software projects, software reliability, software testing},
  pages = {98--100}
}

{"_id":"DNtkKPpYkPSXNS7KS","bibbaseid":"gras-endtoenddefectmodeling-2004","downloads":0,"creationDate":"2015-04-15T11:01:18.493Z","title":"End-to-end defect modeling","author_short":["Gras, J."],"year":2004,"bibtype":"article","biburl":"http://bibbase.org/zotero/troeger","bibdata":{"abstract":"In this context, computer models can help us predict outcomes and anticipate with confidence. We can now use cause-effect modeling to drive software quality, moving our organization toward higher maturity levels. Despite missing good software quality models, many software projects successfully deliver software on time and with acceptable quality. Although researchers have devoted much attention to analyzing software projects' failures, we also need to understand why some are successful - within budget, of high quality, and on time-despite numerous challenges. Restricting software quality to defects, decisions made in successful projects must be based on some understanding of cause-effect relationships that drive defects at each stage of the process. To manage software quality by data, we need a model describing which factors drive defect introduction and removal in the life cycle, and how they do it. Once properly built and validated, a defect model enables successful anticipation. This is why it's important that the model include all variables influencing the process response to some degree.","author":["Gras, J.-J."],"author_short":["Gras, J."],"bibtex":"@article{ gras_end--end_2004,\n title = {End-to-end defect modeling},\n volume = {21},\n issn = {0740-7459},\n doi = {10.1109/MS.2004.1331312},\n abstract = {In this context, computer models can help us predict outcomes and anticipate with confidence. We can now use cause-effect modeling to drive software quality, moving our organization toward higher maturity levels. Despite missing good software quality models, many software projects successfully deliver software on time and with acceptable quality. Although researchers have devoted much attention to analyzing software projects' failures, we also need to understand why some are successful - within budget, of high quality, and on time-despite numerous challenges. Restricting software quality to defects, decisions made in successful projects must be based on some understanding of cause-effect relationships that drive defects at each stage of the process. To manage software quality by data, we need a model describing which factors drive defect introduction and removal in the life cycle, and how they do it. Once properly built and validated, a defect model enables successful anticipation. This is why it's important that the model include all variables influencing the process response to some degree.},\n number = {5},\n journal = {IEEE Software},\n author = {Gras, J.-J.},\n month = {September},\n year = {2004},\n keywords = {010, 05, 100, 210, 415, 416, 460, 65, Bayesian methods, Databases, Gras, J., Journal paper, Predictive models, Project management, Software Quality, Software development management, Software measurement, Software systems, System testing, Time measurement, Using cause-effect modeling to drive software quality can move your organization to higher maturity levels., _done, _meta, cause-effect analysis, cause-effect model, cause-effect modeling, end-to-end defect modeling, process control, software projects, software reliability, software testing},\n pages = {98--100}\n}","bibtype":"article","doi":"10.1109/MS.2004.1331312","id":"gras_end--end_2004","issn":"0740-7459","journal":"IEEE Software","key":"gras_end--end_2004","keywords":"010, 05, 100, 210, 415, 416, 460, 65, Bayesian methods, Databases, Gras, J., Journal paper, Predictive models, Project management, Software Quality, Software development management, Software measurement, Software systems, System testing, Time measurement, Using cause-effect modeling to drive software quality can move your organization to higher maturity levels., _done, _meta, cause-effect analysis, cause-effect model, cause-effect modeling, end-to-end defect modeling, process control, software projects, software reliability, software testing","month":"September","number":"5","pages":"98--100","title":"End-to-end defect modeling","type":"article","volume":"21","year":"2004","bibbaseid":"gras-endtoenddefectmodeling-2004","role":"author","urls":{},"keyword":["010","05","100","210","415","416","460","65","Bayesian methods","Databases","Gras","J.","Journal paper","Predictive models","Project management","Software Quality","Software development management","Software measurement","Software systems","System testing","Time measurement","Using cause-effect modeling to drive software quality can move your organization to higher maturity levels.","_done","_meta","cause-effect analysis","cause-effect model","cause-effect modeling","end-to-end defect modeling","process control","software projects","software reliability","software testing"],"downloads":0},"search_terms":["end","end","defect","modeling","gras"],"keywords":["010","05","100","210","415","416","460","65","bayesian methods","databases","gras","j.","journal paper","predictive models","project management","software quality","software development management","software measurement","software systems","system testing","time measurement","using cause-effect modeling to drive software quality can move your organization to higher maturity levels.","_done","_meta","cause-effect analysis","cause-effect model","cause-effect modeling","end-to-end defect modeling","process control","software projects","software reliability","software testing"],"authorIDs":[],"dataSources":["zHc5HdgBCDgkarEqH"]}