Understanding COBOL systems using inferred types

In a typical COBOL program, the data division consists of 50 of the lines of code. Automatic type inference can help to understand the large collections of variable declarations contained therein, showing how variables are related based on their actual usage. The most problematic aspect of type inference is emph{pollution, the phenomenon that types become too large, and contain variables that intuitively should not belong to the same type. The aim of the paper is to provide empirical evidence for the hypothesis that the use of emph{subtyping is an effective way for dealing with pollution. The main results include a tool set to carry out type inference experiments, a suite of metrics characterizing type inference outcomes, and the conclusion that only one instance of pollution was found in the case study conducted