Applying plan recognition algorithms to program understanding

Program understanding is often viewed as the task of extracting plans and design goals from program source. As such, it is natural to try to apply standard AI plan recognition techniques to the program understanding problem. Yet program understanding researchers have quietly, but consistently, avoided the use of these plan recognition algorithms. This paper shows that treating program understanding as plan recognition is too simplistic and that traditional AI search algorithms for plan recognition are not suitable. In particular, we show that: the program understanding task differs significantly from the typical general plan recognition task along several key dimensions; the program understanding task has particular properties that make it particularly amenable to constraint satisfaction techniques; and augmenting AI plan recognition algorithms with these techniques can lead to effective solutions for the program understanding problem.