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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.