Enhancing program comprehension with recovered state models

Abstract

State transition machines are high-level behavior descriptions often used as modeling tools for the design and implementation of a large class of software systems. Some of the state transition machine implementation approaches are such that the static structure of the resulting code closely matches that of the original state transition machines. Therefore, having a representation of the original state transition machines is likely to improve the corresponding code understandability. We present an approach supported by a prototype tool, to extract state transition machines by static analysis of source code. An objective of this work is to enhance program comprehension with visual representations of the behavior of the programs being analyzed.