We study four transformation methodologies which are automatic instances of Burstall and Darlington's fold/unfold framework: partial evaluation, deforestation, supercompilation, and generalized partial computation (GPC). One can classify these and other fold/unfold based transformers by how much information they maintain during transformation. We introduce the positive supercompiler, a version of deforestation including more information propagation, to study such a classification in detail. Via the study of positive supercompilation we are able to show that partial evaluation and deforestation have simple information propagation, positive supercompilation has more information propagation, and supercompilation and GPC have even more information propagation. The amount of information propagation is significant: positive supercompilation, GPC, and supercompilation can specialize a general pattern matcher to a fixed pattern so as to obtain efficient output similar to that of the Knuth-Morris-Pratt algorithm. In the case of partial evaluation and deforestation, the general matcher must be rewritten to achieve this.