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Over the past decades, context sensitivity has been considered as one of themost effective ideas for improving the precision of pointer analysis for Java.However, despite great precision benefits, as each method is equivalentlycloned and analyzed under each context, context sensitivity brings heavyefficiency costs. In this work, we present a fundamentally different approachcalled Cut-Shortcut for fast and precise pointer analysis for Java. Its insightis simple: the main effect of cloning methods under different contexts is tofilter spurious object flows that have been merged inside a callee method; fromthe view of a typical pointer flow graph (PFG), such effect can be simulated bycutting off (Cut) the edges that introduce precision loss to certain pointersand adding Shortcut edges directly from source pointers to the target onescircumventing the method on PFG. As a result, we can achieve the effect ofcontext sensitivity without contexts. We identify three general programpatterns and develop algorithms based on them to safely cut off and addshortcut edges on PFG, formalize them and formally prove the soundness. Tocomprehensively validate Cut-Shortcut's effectiveness, we implement twoversions of Cut-Shortcut for two state-of-the-art pointer analysis frameworksfor Java, one in Datalog for the declarative Doop and the other in Java for theimperative Tai-e, and we consider all the large and complex programs used inrecent literatures that meet the experimental requirements. The evaluationresults are extremely promising: Cut-Shortcut is even able to run faster thancontext insensitivity for most evaluated programs while obtaining highprecision that is comparable to context sensitivity (if scalable) in bothframeworks. This is for the first time that we have been able to achieve such agood efficiency and precision trade-off for those hard-to-analyze programs.

Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis