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The use of static analysis allows developers to analyze the source code of a program. On one hand, developers get accurate data representing any issues with the program. On the other hand, static analysis programs can have lengthy execution times. This results in a large amount of developer idleness, waiting for their program to execute. A developer's debug cycles can be particularly time consuming, when tweaking their analyses. They then have to wait around for long periods of time to test their minor changes. In order to avoid this needless idle time, we have looked into modularizing a static analysis program in order to cut down on time wasted.

We propose two efficient algorithms for exploring topic diversity in large document corpora such as user generated content on the social web, bibliographic data, or other web repositories. Analyzing diversity is useful for obtaining insights into knowledge evolution, trends, periodicities, and topic heterogeneity of such collections. Calculating diversity statistics requires averaging over the similarity of all object pairs, which, for large corpora, is prohibitive from a computational point of view. Our proposed algorithms overcome the quadratic complexity of the average pair-wise similarity computation, and allow for constant time (depending on dataset properties) or linear time approximation with probabilistic guarantees.