Compact Representation and Interleaved Solving for Scalable Constraint-Based Points-to Analysis

Constraint-based points-to analysis using Andersen-style inclusion constraints is widely used for its convenience, generality, and precision in modeling complex program behaviors. Typically, such analyses generate constraints and resolve them by computing the transitive closure of a constraint graph. However, traditional constraint modeling often introduces redundancy during constraint generation, solving, or both. In the context of points-to analysis for object-oriented languages like Java, this redundancy primarily stems from the modeling of virtual calls and heap operations. As a result, the analysis produces redundant constraints and inflated constraint graphs, thereby increasing analysis time.