Toward a constraint-satisfaction framework for evaluating program-understanding algorithms

Different program understanding algorithms often use different representational frameworks and take advantage of numerous heuristic tricks. This situation makes it difficult to compare these approaches and their performance. The paper addresses this problem by proposing constraint satisfaction as a general framework for describing program understanding algorithms, demonstrating how to transform a relatively complex existing program understanding algorithm into an instance of a constraint satisfaction problem, and showing how this facilitates better understanding of its performance.