An entropy-based measure of software complexity

It is proposed that the complexity of a program is inversely proportional to the average information content of its operators. An empirical probability distribution of the operators occurring in a program is constructed, and the classical entropy calculation is applied. The performance of the resulting metric is assessed in the analysis of two commercial applications totaling well over 130000 lines of code. The results indicate that the new metric does a good job of associating modules with their error spans (averaging number of tokens between error occurrences).<
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