Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis (2304.12034v1)

Abstract: Over the past decades, context sensitivity has been considered as one of the most effective ideas for improving the precision of pointer analysis for Java. However, despite great precision benefits, as each method is equivalently cloned and analyzed under each context, context sensitivity brings heavy efficiency costs. In this work, we present a fundamentally different approach called Cut-Shortcut for fast and precise pointer analysis for Java. Its insight is simple: the main effect of cloning methods under different contexts is to filter spurious object flows that have been merged inside a callee method; from the view of a typical pointer flow graph (PFG), such effect can be simulated by cutting off (Cut) the edges that introduce precision loss to certain pointers and adding Shortcut edges directly from source pointers to the target ones circumventing the method on PFG. As a result, we can achieve the effect of context sensitivity without contexts. We identify three general program patterns and develop algorithms based on them to safely cut off and add shortcut edges on PFG, formalize them and formally prove the soundness. To comprehensively validate Cut-Shortcut's effectiveness, we implement two versions of Cut-Shortcut for two state-of-the-art pointer analysis frameworks for Java, one in Datalog for the declarative Doop and the other in Java for the imperative Tai-e, and we consider all the large and complex programs used in recent literatures that meet the experimental requirements. The evaluation results are extremely promising: Cut-Shortcut is even able to run faster than context insensitivity for most evaluated programs while obtaining high precision that is comparable to context sensitivity (if scalable) in both frameworks. This is for the first time that we have been able to achieve such a good efficiency and precision trade-off for those hard-to-analyze programs.