A classification procedure for the effective management of changes during the maintenance process

The authors address the issue of assessing the difficulty of a change based on known or predictable data. They consider the present work as a first step towards the construction of customized economic models for maintainers. They propose a modeling approach, based on regular statistical techniques, that can be used in a variety of software maintenance environments. This approach can be easily automated, and is simple for people with limited statistical experience to use. Moreover, it deals effectively with the uncertainty usually associated with both model inputs and outputs. The modeling approach is validated on a data set provided by the NASA Goddard Space Flight Center, showing that it has been effective in classifying changes with respect to the effort involved in implementing them. Other advantages of the approach are discussed along with additional steps to improve the results.