RESUMEN

Software measurement has the potential to play an important role in risk management during product development. Metrics incorporated into predictive models can give advanced warning of potential risks. However, the common approach of using simple regression models, notably to predict software defects, can lead to inappropriate risk management decisions. These naïve models should be replaced with predictive models incorporating genuine cause-effect relationships. The authors show how to build these models using Bayesian networks, a powerful graphical modeling technique for software quality risk management that is providing accurate predictions of software defects in a range of real projects. As well as their use for prediction, Bayesian networks can also be used for performing a range of "what if" scenarios to identify potential problems and possible improvement actions.