Scalability-First Pointer Analysis with Self-Tuning Context-Sensitivity
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.
Tue 6 NovDisplayed time zone: Guadalajara, Mexico City, Monterrey change
13:30 - 15:00
Software Analysis IJournal-First / Research Papers at Horizons 5
Chair(s): Sebastian Elbaum University of Nebraska-Lincoln, USA
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Ganesha Upadhyaya Futurewei Technologies, Hridesh Rajan Iowa State UniversityDOI
|RefiNym: Using Names to Refine Types|
Santanu Kumar Dash University College London, UK, Miltiadis Allamanis Microsoft Research, Cambridge, Earl T. Barr
|Darwinian Data Structure Selection|
Michail Basios University College London, Lingbo Li University College London, UK, Fan Wu University College London, UK, Leslie Kanthan University College London, UK, Earl T. BarrDOI Pre-print
|Scalability-First Pointer Analysis with Self-Tuning Context-Sensitivity|
Yue Li Aarhus University, Denmark, Tian Tan Aarhus University, Denmark, Anders Møller Aarhus University, Yannis Smaragdakis University of Athens