Program Complexity Measures Based on Information Theory     Complexity Measures
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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.
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[{'Code': '2880', 'Text': 'SOFTWARE ENGINEERING AND LANGU'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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