Reverse engineering concurrent UML state machines using black box testing and genetic programming

Abstract

This paper presents a technique for reverse engineering, a software system generated from a concurrent unified modeling language state machine implementation. In its first step, a primitive sequential finite-state machine (FSM) is deduced from a sequence of outputs emitted from black box tests applied to the systems’ input interface. Next, we provide an algorithmic technique for decomposing the sequential primitive FSM into a set of concurrent (orthogonal) primitive FSMs. Lastly, we show a genetic programming machine learning technique for discovering local variables, actions performed on local and non-binary output variables, and two types of intra-FSM loops, called counting-loops and while-loops.