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Computing precise (fully flow-sensitive and context-sensitive) and exhaustivepoints-to information is computationally expensive. Many practical toolsapproximate the points-to information trading precision for efficiency. Thishas adverse impact on computationally intensive analyses such as modelchecking. Past explorations in top-down approaches of fully flow- andcontext-sensitive points-to analysis (FCPA) have not scaled. We explore thealternative of bottom-up interprocedural approach which constructs summary flowfunctions for procedures to represent the effect of their calls. This approachhas been effectively used for many analyses. However, it is computationallyexpensive for FCPA which requires modelling unknown locations accessedindirectly through pointers. Such accesses are commonly handled by usingplaceholders to explicate unknown locations or by using multiple call-specificsummary flow functions.  We generalize the concept of points-to relations by using the counts ofindirection levels leaving the unknown locations implicit. This allows us tocreate summary flow functions in the form of generalized points-to graphs(GPGs) without the need of placeholders. By design, GPGs represent both memory(in terms of classical points-to facts) and memory transformers (in terms ofgeneralized points-to facts). We perform FCPA by progressively reducinggeneralized points-to facts to classical points-to facts. GPGs distinguishbetween may and must pointer updates thereby facilitating strong updates withincalling contexts.  The size of GPG is linearly bounded by the number of variables and isindependent of the number of statements in the procedure. Empiricalmeasurements on SPEC benchmarks show that GPGs are indeed compact in spite oflarge procedure sizes. This allows us to scale FCPA to 158 kLoC using GPGs(compared to 35 kLoC reported by liveness-based FCPA).

Flow- and Context-Sensitive Points-to Analysis using Generalized Points-to Graphs