This project is based on the hypothesis that the prime program decomposition of a program provides a theoretic programmatic basis for an effective complexity measure that closely correlates with our intuitive notion of program complexity, and is related to the economic issues of reliability, productivity and cost. This measure is based upon the information theory ideas of randomness and entropy. The basic idea is to develop a theory such that results about structured programming, data abstractions and other programming paradigms can be stated in quantitative terms and empirical means can be used to validate the assumptions used to develop the model. There is a three- phase approach: (a) Developing the theorectical model; (b) Adapting the model to real-world programming languages; and (c) Evaluating the model with existing programming examples.
