Partitioned Memory Models for Program Analysis

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10145
• 期：1
• 页码：539-558
• 丛书名：Verification, Model Checking, and Abstract Interpretation
• ISBN：978-3-319-52234-0
• 卷排序：10145

文摘

Scalability is a key challenge in static analysis. For imperative languages like C, the approach taken for modeling memory can play a significant role in scalability. In this paper, we explore a family of memory models called partitioned memory models which divide memory up based on the results of a points-to analysis. We review Steensgaard’s original and field-sensitive points-to analyses as well as Data Structure Analysis (DSA), and introduce a new cell-based points-to analysis which more precisely handles heap data structures and type-unsafe operations like pointer arithmetic and pointer casting. We give experimental results on benchmarks from the software verification competition using the program verification framework in Cascade. We show that a partitioned memory model using our cell-based points-to analysis outperforms models using other analyses.