  Industrial process engineering and PLC program development have traditionally
favored Function Block Diagram (FBD) programming over classical imperative
style programming like the object oriented and functional programming
paradigms. The increasing momentum in the adoption and trial of ideas now
classified as 'No Code' or 'Low Code' alongside the mainstream success of
statistical learning theory or the so-called machine learning is redefining the
way in which we structure programs for the digital machine to execute. A
principal focus of 'No Code' is deriving executable programs directly from a
set of requirement documents or any other documentation that defines consumer
or customer expectation. We present a method for generating Function Block
Diagram (FBD) programs as either the intermediate or final artifact that can be
executed by a target system from a set of requirement documents using a
constrained selection algorithm that draws from the top line of an associated
recommender system. The results presented demonstrate that this type of No-code
generative model is a viable option for industrial process design.
