Some experiments toward understanding how program plan recognition algorithms scale

Over the past decade, researchers in program understanding have formulated many program understanding algorithms but have published few studies of their relative scalability. Consequently, it is difficult to understand the relative limitations of these algorithms and to determine whether the field of program understanding is making progress. The paper attempts to address this deficiency by formalizing the search strategies of several different program understanding algorithms as constraint satisfaction problems, and by presenting some preliminary empirical scalability results for these constraint-based implementations. These initial results suggest that, at least under certain conditions, constraint-based program understanding is close to being applicable to real-world programs.