Most of the considerable work that has been done in the measurement of software complexity during the past several years has addressed complexity measurement of source code or design languages. Here we describe techniques to measure the complexity of large (>100,000 source lines of code) systems during the software architecture phase, before major design decisions have been made. The techniques to measure and reduce complexity are intuitively reasonable, easy to apply, and produce consistent results. Methods developed include (1) an extension of the graph-theoretic measure developed by McCabe to software architecture, as represented by networks of communicating modules, (2) a general technique that allows the complexity associated with allocation of resources (CPU, tape, disk, etc.) to be measured, and (3) a method that combines module complexity and network complexity, so that design trade-offs can be studied to determine whether it is advantageous to have separate modules for service functions, such as mathematical subroutines, data management routines, etc.
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