Do Extracted State Machine Models Help to Understand Embedded Software?

Abstract

Program understanding is a prerequisite for several software activities, such as maintenance, evolution, and reengineering. Code in itself is so detailed that it is often hard to understand. More abstract models describing its behaviour may ease program understanding. Manually building understandable abstractions from complex source code – as an explicit or just mental model – requires in-depth analysis of the code in the first place. Therefore, it is a time-consuming and tedious activity for developers. Model mining can support program comprehension by semi-automatically extracting high-level models from code. One helpful model is a state machine, which is an established formalism for specifying the behaviour of a software component. In this paper, we report on a controlled experiment that investigates the question: Do semi-automatically extracted state machines make understanding of complex embedded code more effective? The experiment was conducted with 30 participants on two industrial embedded C code functions. The results show that the share of correct answers increases and the required time to solve the tasks decreases significantly when extracted state machines are available. We conclude that mined state machines do in fact help in program understanding.