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Opis

Pointer analysis is one of the key static analyses during compilation and affects scalability and precision of several client transformations. Recent advances still lack an efficient and scalable context-sensitive inclusion-based pointer analysis. In this work, we propose four novel techniques to improve the scalability of context-sensitive points-to analysis for C/C++ programs. First, we develop an efficient way of storing the approximate points-to information using a multi-dimensional bloom filter (multibloom). Second, we devise a sound randomized algorithm that processes a group of constraints in a less precise but efficient manner and the remaining constraints in a more precise manner. Third, we transform the points-to analysis problem into finding a solution to a system of linear equations. Finally, we observe that the order in which points-to constraints are processed plays a vital role in the algorithm efficiency and propose a greedy heuristic based on the amount of points-to information computed by a constraint to prioritize the constraints. We illustrate that our algorithms help in scaling the state-of-the-art pointer analyses.

Szczegóły książki

• Pełny tytuł: Scaling Context-Sensitive Points-to Analysis
• Podtytuł: Novel ideas to tame the scalability challenges
• Autor: Rupesh Nasre
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 212
• EAN: 9783659977183
• ID: 15359266
• Wydawca: LAP Lambert Academic Publishing
• Waga: 335 g
• Wymiary: 220 × 150 × 12 mm
• Rok wydania: 2016

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