Scalability-First Pointer Analysis with Self-Tuning Context-Sensitivity

Yue Li, Tian Tan, Anders Møller and Yannis Smaragdakis

Context-sensitivity is important in pointer analysis to ensure high precision, but existing techniques suffer from unpredictable scalability. Many variants of context-sensitivity exist, and it is difficult to choose one that leads to reasonable analysis time and obtains high precision, without running the analysis multiple times.

We present the Scaler framework that addresses this problem. Scaler efficiently estimates the amount of points-to information that would be needed to analyze each method with different variants of context-sensitivity. It then selects an appropriate variant for each method so that the total amount of points-to information is bounded, while utilizing the available space to maximize precision. Our experimental results demonstrate that Scaler achieves predictable scalability for all the evaluated programs (e.g., speedups can reach 10x for 2-object-sensitivity), while providing a precision that matches or even exceeds that of the best alternative techniques.

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