Accelerating the learning curve of software maintainers working on systems with which they have little familiarity motivated this study. A working hypothesis was that automated methods are needed to provide a fast, rough grasp of a system, to enable practitioners not familiar with it, to commence maintenance with a level of confidence as if they had this familiarity.

Expert maintainers were interviewed regarding their strategies and information needs to test this hypothesis. The overriding message is their need for a "starting point" when analyzing code. They also need standardized, reliable and communicable information about a system as an equivalent to knowledge available only to developers or experienced maintainers. These needs are addressed by the proposed "rough-cut" approach to program comprehension. Work underway assesses the suitability of using data mining techniques on data derived from source code to provide high level models of a system and module interrelationships.