A Method for Choosing Software Assessment Measures Using Bayesian Networks and Diagnosis

Abstract

Creating accurate models of information systems is an important but challenging task. While the scientific aspects of such modeling are generally acknowledged, the monetary aspects of the modeling of software systems are not. The present paper describes a Bayesian method for optimizing modeling strategies, perceived as a trade-off between these two aspects. Specifically, an informed trade-off can be made, based on the modeler's prior knowledge of the predictive power of certain models, combined with her projection of the costs. It is argued that this method enhances modeling of large and complex software systems in two principal ways: Firstly, by enforcing rigor and making hidden assumptions explicit. Secondly, by enforcing cost awareness even in the early phases of modeling. The method should be used primarily when the choice of modeling can have great economic repercussions.