Clarity Guided Belief Revision for Domain Knowledge Recovery in Legacy Systems

Program understanding is the process of acquiring knowledge from a computer program. Although research work utilising knowledge engineering techniques has been undertaken in this field, it is our observation that a thorough application of AI methodology has not been sufficiently explored. In this paper, we present a clarity guided belief revision approach to domain knowledge recovery in legacy software systems. Novel solutions are given to three key AI issues in the context of domain knowledge recovery from source code: knowledge representation, where concrete semantic network is separated from abstract semantic network to better accommodate uncertainty reasoning and propagation; uncertainty reasoning, which borrows ideas from confirmation theory and recasts them in the context of semantic network reasoning; heuristic search, which is designed on the principle of programming psychology. Our approach is light-weighted. It can be used stand-alone or as a complement to traditional heavy-weighted domain knowledge recovery methods.