Defect cost flow model: a Bayesian network for predicting defect correction effort

Background. Software defect prediction has been one of the central topics of software engineering. Predicted defect counts have been used mainly to assess software quality and estimate the defect correction effort (DCE). However, in many cases these defect counts are not good indicators for DCE. Therefore, in this study DCE has been modeled from a different perspective. Defects originating from various development phases have different impact on the overall DCE, especially defects shifting from one phase to another. To reduce the DCE of a software product it is important to assess every development phase along with its specific characteristics and focus on the shift of defects over phases. Aims. The aim of this paper is to build a model for effort prediction at different development stages. Our model is mainly focused on a dynamic DCE changing from one development phase to another. It reflects the increasing cost of correcting defects which are introduced in early, but found in later ...

• Type: Journal
• Year: 2010
• Where: PROMISE
• Authors: Thomas Schulz, Lukasz Radlinski, Thomas Gorges, Wolfgang Rosenstiel
• Keywords: Development Phases, Empirical Data, Model, PROMISE 2010, Software Engineering