A potential way to reduce this consternation is to independently deploy the algorithm as a new piece of software. By rigorously documenting the development of the new code, adhering to a set of software engineering principles, and regularly subjecting working code to peer review, a finished product which produces results similar to the original code should increase confidence and trust in the method. Using a modern, widely-adopted language (such as Python) and well-documented libraries can also help de-mystify the code, leading to more transparency.
I performed this development process on the aforementioned pairwise homogenization code and implemented the core algorithm in pure Python. In this talk, I will detail considerations that went into this development, both numerical (precision and adjustments to methods necessary when switching from Fortran to Python) and computational (improving performance through better design philosophy, the multiprocessing library, and potential use for NumPy). I will also detail how the independent development process helped identify bugs and glitches in the original code which had previously been overlooked, and the practicality and potential benefits of porting complex legacy codes - like the homogenization routine - to a modern codebase.
Supplementary URL: http://code.google.com/p/ccf-homogenization/