Comparing methods to identify defect reports in a change management database

A key problem when doing automated fault analysis and fault prediction from information in a software change management database is how to determine which change reports represent software faults. In some change management systems, there is no simple way to distinguish fault reports from changes made to add new functionality or perform routine maintenance. This paper describes a comparison of two methods for classifying change reports for a large software system, and concludes that, for that particular system, the stage of development when the report was initialized is a more accurate indicator of its fault status than the presence of certain keywords in the report's natural language description.