Abstract
In this paper, we propose a model that formalizes the role of software evolution in characterizing Technical Debt (TD) by defining a series of software product states, where each successive state represents an increased level of maintenance code churn, and thus presumably an increased level of change difficulty. We also propose a way to use these states to estimate TD principal and interest and use this information in decision making during release planning. In addition, we illustrate our model using bug report data from the Eclipse-Birt project.