Handling large search space in pattern-based reverse engineering

Abstract

Large industrial legacy systems are challenges of reverse-engineering activities. Reverse-engineering approaches use text-search tools based on regular expressions or work on graph representations of programs, such as abstract syntax graphs. Analyzing large legacy systems often fail because of the large search space. Our approach to handle large search space in pattern-based reverse engineering is to allow imprecise results in means of false positives. We use the theory of fuzzy sets to express impreciseness and present our approach on the example of recovering associations.